Book

A graph similarity for deep learning Seongmin Ok

An Unsupervised Information-Theoretic Perceptual Quality Metric Sangnie Bhardwaj, Ian Fischer, Johannes Ballé, Troy Chinen

Self-Supervised MultiModal Versatile Networks Jean-Baptiste Alayrac, Adria Recasens, Rosalia Schneider, Relja Arandjelović, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, Andrew Zisserman

Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method Simiao Ren, Willie Padilla, Jordan Malof

Off-Policy Evaluation and Learning for External Validity under a Covariate Shift Masatoshi Uehara, Masahiro Kato, Shota Yasui

Neural Methods for Point-wise Dependency Estimation Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada, Louis-Philippe Morency, Russ R. Salakhutdinov

Fast and Flexible Temporal Point Processes with Triangular Maps Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann

Backpropagating Linearly Improves Transferability of Adversarial Examples Yiwen Guo, Qizhang Li, Hao Chen

PyGlove: Symbolic Programming for Automated Machine Learning Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, Quoc Le

Fourier Sparse Leverage Scores and Approximate Kernel Learning Tamas Erdelyi, Cameron Musco, Christopher Musco

Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds Nicholas Harvey, Christopher Liaw, Tasuku Soma

Synbols: Probing Learning Algorithms with Synthetic Datasets Alexandre Lacoste, Pau Rodríguez López, Frederic Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Hadj Laradji, Alexandre Drouin, Matthew Craddock, Laurent Charlin, David Vázquez

Adversarially Robust Streaming Algorithms via Differential Privacy Avinatan Hasidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering Long Chen, Yuan Yao, Feng Xu, Miao Xu, Hanghang Tong

Cascaded Text Generation with Markov Transformers Yuntian Deng, Alexander Rush

Improving Local Identifiability in Probabilistic Box Embeddings Shib Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum

Permute-and-Flip: A new mechanism for differentially private selection Ryan McKenna, Daniel R. Sheldon

Deep reconstruction of strange attractors from time series William Gilpin

Reciprocal Adversarial Learning via Characteristic Functions Shengxi Li, Zeyang Yu, Min Xiang, Danilo Mandic

Statistical Guarantees of Distributed Nearest Neighbor Classification Jiexin Duan, Xingye Qiao, Guang Cheng

Stein Self-Repulsive Dynamics: Benefits From Past Samples Mao Ye, Tongzheng Ren, Qiang Liu

The Statistical Complexity of Early-Stopped Mirror Descent Tomas Vaskevicius, Varun Kanade, Patrick Rebeschini

Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera

Quantitative Propagation of Chaos for SGD in Wide Neural Networks Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli

A Causal View on Robustness of Neural Networks Cheng Zhang, Kun Zhang, Yingzhen Li

Minimax Classification with 0-1 Loss and Performance Guarantees Santiago Mazuelas, Andrea Zanoni, Aritz Pérez

How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization Pierluca D'Oro, Wojciech Jaśkowski

Coresets for Regressions with Panel Data Lingxiao Huang, K Sudhir, Nisheeth Vishnoi

Learning Composable Energy Surrogates for PDE Order Reduction Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams

Efficient Contextual Bandits with Continuous Actions Maryam Majzoubi, Chicheng Zhang, Rajan Chari, Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins

Achieving Equalized Odds by Resampling Sensitive Attributes Yaniv Romano, Stephen Bates, Emmanuel Candes

Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates Wenhao Luo, Wen Sun, Ashish Kapoor

Hard Shape-Constrained Kernel Machines Pierre-Cyril Aubin-Frankowski, Zoltan Szabo

A Closer Look at the Training Strategy for Modern Meta-Learning JIAXIN CHEN, Xiao-Ming Wu, Yanke Li, Qimai LI, Li-Ming Zhan, Fu-lai Chung

On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law Damien Teney, Ehsan Abbasnejad, Kushal Kafle, Robik Shrestha, Christopher Kanan, Anton van den Hengel

Generalised Bayesian Filtering via Sequential Monte Carlo Ayman Boustati, Omer Deniz Akyildiz, Theodoros Damoulas, Adam Johansen

Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time Kai Han, zongmai Cao, Shuang Cui, Benwei Wu

Flows for simultaneous manifold learning and density estimation Johann Brehmer, Kyle Cranmer

Simultaneous Preference and Metric Learning from Paired Comparisons Austin Xu, Mark Davenport

Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee Jincheng Bai, Qifan Song, Guang Cheng

Learning Manifold Implicitly via Explicit Heat-Kernel Learning Yufan Zhou, Changyou Chen, Jinhui Xu

Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network Chaojie Wang, Hao Zhang, Bo Chen, Dongsheng Wang, Zhengjue Wang, Mingyuan Zhou

One-bit Supervision for Image Classification Hengtong Hu, Lingxi Xie, Zewei Du, Richang Hong, Qi Tian

What is being transferred in transfer learning? Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang

Submodular Maximization Through Barrier Functions Ashwinkumar Badanidiyuru, Amin Karbasi, Ehsan Kazemi, Jan Vondrak

Neural Networks with Recurrent Generative Feedback Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan Nguyen, Doris Tsao, Anima Anandkumar

Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction Jinheon Baek, Dong Bok Lee, Sung Ju Hwang

Exploiting weakly supervised visual patterns to learn from partial annotations Kaustav Kundu, Joseph Tighe

Improving Inference for Neural Image Compression Yibo Yang, Robert Bamler, Stephan Mandt

Neuron Merging: Compensating for Pruned Neurons Woojeong Kim, Suhyun Kim, Mincheol Park, Geunseok Jeon

FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A. Raffel, Ekin Dogus Cubuk, Alexey Kurakin, Chun-Liang Li

Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing Arthur Delarue, Ross Anderson, Christian Tjandraatmadja

Towards Playing Full MOBA Games with Deep Reinforcement Learning Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu

Rankmax: An Adaptive Projection Alternative to the Softmax Function Weiwei Kong, Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang

Online Agnostic Boosting via Regret Minimization Nataly Brukhim, Xinyi Chen, Elad Hazan, Shay Moran

Causal Intervention for Weakly-Supervised Semantic Segmentation Dong Zhang, Hanwang Zhang, Jinhui Tang, Xian-Sheng Hua, Qianru Sun

Belief Propagation Neural Networks Jonathan Kuck, Shuvam Chakraborty, Hao Tang, Rachel Luo, Jiaming Song, Ashish Sabharwal, Stefano Ermon

Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality Yi Zhang, Orestis Plevrakis, Simon S. Du, Xingguo Li, Zhao Song, Sanjeev Arora

Post-training Iterative Hierarchical Data Augmentation for Deep Networks Adil Khan, Khadija Fraz

Debugging Tests for Model Explanations Julius Adebayo, Michael Muelly, Ilaria Liccardi, Been Kim

Robust compressed sensing using generative models Ajil Jalal, Liu Liu, Alexandros G. Dimakis, Constantine Caramanis

Fairness without Demographics through Adversarially Reweighted Learning Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed Chi

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine

Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alexander Peysakhovich, Aldo Pacchiano, Jakob Foerster

The route to chaos in routing games: When is price of anarchy too optimistic? Thiparat Chotibut, Fryderyk Falniowski, Michał Misiurewicz, Georgios Piliouras

Online Algorithm for Unsupervised Sequential Selection with Contextual Information Arun Verma, Manjesh Kumar Hanawal, Csaba Szepesvari, Venkatesh Saligrama

Adapting Neural Architectures Between Domains Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu

What went wrong and when? Instance-wise feature importance for time-series black-box models Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David K. Duvenaud, Anna Goldenberg

Towards Better Generalization of Adaptive Gradient Methods Yingxue Zhou, Belhal Karimi, Jinxing Yu, Zhiqiang Xu, Ping Li

Learning Guidance Rewards with Trajectory-space Smoothing Tanmay Gangwani, Yuan Zhou, Jian Peng

Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization Chaobing Song, Yong Jiang, Yi Ma

Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding Rishi Sonthalia, Anna Gilbert

Deep Structural Causal Models for Tractable Counterfactual Inference Nick Pawlowski, Daniel Coelho de Castro, Ben Glocker

Convolutional Generation of Textured 3D Meshes Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurelien Lucchi

A Statistical Framework for Low-bitwidth Training of Deep Neural Networks Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, Joseph E. Gonzalez

Better Set Representations For Relational Reasoning Qian Huang, Horace He, Abhay Singh, Yan Zhang, Ser Nam Lim, Austin R. Benson

AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning Hao Zhang, Yuan Li, Zhijie Deng, Xiaodan Liang, Lawrence Carin, Eric Xing

A Combinatorial Perspective on Transfer Learning Jianan Wang, Eren Sezener, David Budden, Marcus Hutter, Joel Veness

Hardness of Learning Neural Networks with Natural Weights Amit Daniely, Gal Vardi

Higher-Order Spectral Clustering of Directed Graphs Steinar Laenen, He Sun

Primal-Dual Mesh Convolutional Neural Networks Francesco Milano, Antonio Loquercio, Antoni Rosinol, Davide Scaramuzza, Luca Carlone

The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto

Watch out! Motion is Blurring the Vision of Your Deep Neural Networks Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Jian Wang, Bing Yu, Wei Feng, Yang Liu

Sinkhorn Barycenter via Functional Gradient Descent Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani

Coresets for Near-Convex Functions Murad Tukan, Alaa Maalouf, Dan Feldman

Bayesian Deep Ensembles via the Neural Tangent Kernel Bobby He, Balaji Lakshminarayanan, Yee Whye Teh

Improved Schemes for Episodic Memory-based Lifelong Learning Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing

Adaptive Sampling for Stochastic Risk-Averse Learning Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause

Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring Jiangxin Dong, Stefan Roth, Bernt Schiele

Discovering Reinforcement Learning Algorithms Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado P. van Hasselt, Satinder Singh, David Silver

Taming Discrete Integration via the Boon of Dimensionality Jeffrey Dudek, Dror Fried, Kuldeep S Meel

Blind Video Temporal Consistency via Deep Video Prior Chenyang Lei, Yazhou Xing, Qifeng Chen

Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, Depeng Jin

Model Selection for Production System via Automated Online Experiments Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Benjamin Carterette, Mounia Lalmas

On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems Panayotis Mertikopoulos, Nadav Hallak, Ali Kavis, Volkan Cevher

Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond Kaidi Xu, Zhouxing Shi, Huan Zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh

Adaptation Properties Allow Identification of Optimized Neural Codes Luke Rast, Jan Drugowitsch

Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems Junchi Yang, Negar Kiyavash, Niao He

Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity Kaiqing Zhang, Sham Kakade, Tamer Basar, Lin Yang

Conservative Q-Learning for Offline Reinforcement Learning Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine

Online Influence Maximization under Linear Threshold Model Shuai Li, Fang Kong, Kejie Tang, Qizhi Li, Wei Chen

Ensembling geophysical models with Bayesian Neural Networks Ushnish Sengupta, Matt Amos, Scott Hosking, Carl Edward Rasmussen, Matthew Juniper, Paul Young

Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation Yuxi Li, Ning Xu, Jinlong Peng, John See, Weiyao Lin

Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability Christopher Frye, Colin Rowat, Ilya Feige

Understanding Deep Architecture with Reasoning Layer Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song

Planning in Markov Decision Processes with Gap-Dependent Sample Complexity Anders Jonsson, Emilie Kaufmann, Pierre Menard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko

Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill

Detection as Regression: Certified Object Detection with Median Smoothing Ping-yeh Chiang, Michael Curry, Ahmed Abdelkader, Aounon Kumar, John Dickerson, Tom Goldstein

Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab Mirrokni

ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann

FleXOR: Trainable Fractional Quantization Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Yongkweon Jeon, Baeseong Park, Jeongin Yun

The Implications of Local Correlation on Learning Some Deep Functions Eran Malach, Shai Shalev-Shwartz

Learning to search efficiently for causally near-optimal treatments Samuel Håkansson, Viktor Lindblom, Omer Gottesman, Fredrik D. Johansson

A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses Ambar Pal, Rene Vidal

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts Bertrand Charpentier, Daniel Zügner, Stephan Günnemann

Recurrent Quantum Neural Networks Johannes Bausch

No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Thanasis Lianeas, Panayotis Mertikopoulos, Georgios Piliouras

A Unifying View of Optimism in Episodic Reinforcement Learning Gergely Neu, Ciara Pike-Burke

Continuous Submodular Maximization: Beyond DR-Submodularity Moran Feldman, Amin Karbasi

An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits Andrea Tirinzoni, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric

Assessing SATNet's Ability to Solve the Symbol Grounding Problem Oscar Chang, Lampros Flokas, Hod Lipson, Michael Spranger

A Bayesian Nonparametrics View into Deep Representations Michał Jamroż, Marcin Kurdziel, Mateusz Opala

On the Similarity between the Laplace and Neural Tangent Kernels Amnon Geifman, Abhay Yadav, Yoni Kasten, Meirav Galun, David Jacobs, Basri Ronen

A causal view of compositional zero-shot recognition Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik

HiPPO: Recurrent Memory with Optimal Polynomial Projections Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, Christopher Ré

Auto Learning Attention Benteng Ma, Jing Zhang, Yong Xia, Dacheng Tao

CASTLE: Regularization via Auxiliary Causal Graph Discovery Trent Kyono, Yao Zhang, Mihaela van der Schaar

Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect Kaihua Tang, Jianqiang Huang, Hanwang Zhang

Explainable Voting Dominik Peters, Ariel D. Procaccia, Alexandros Psomas, Zixin Zhou

Deep Archimedean Copulas Chun Kai Ling, Fei Fang, J. Zico Kolter

Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization Ben Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy

UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi

Thunder: a Fast Coordinate Selection Solver for Sparse Learning Shaogang Ren, Weijie Zhao, Ping Li

Neural Networks Fail to Learn Periodic Functions and How to Fix It Liu Ziyin, Tilman Hartwig, Masahito Ueda

Distribution Matching for Crowd Counting Boyu Wang, Huidong Liu, Dimitris Samaras, Minh Hoai Nguyen

Correspondence learning via linearly-invariant embedding Riccardo Marin, Marie-Julie Rakotosaona, Simone Melzi, Maks Ovsjanikov

Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Xu Chi

On Adaptive Attacks to Adversarial Example Defenses Florian Tramer, Nicholas Carlini, Wieland Brendel, Aleksander Madry

Sinkhorn Natural Gradient for Generative Models Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani

Online Sinkhorn: Optimal Transport distances from sample streams Arthur Mensch, Gabriel Peyré

Ultrahyperbolic Representation Learning Marc Law, Jos Stam

Locally-Adaptive Nonparametric Online Learning Ilja Kuzborskij, Nicolò Cesa-Bianchi

Compositional Generalization via Neural-Symbolic Stack Machines Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, Denny Zhou

Graphon Neural Networks and the Transferability of Graph Neural Networks Luana Ruiz, Luiz Chamon, Alejandro Ribeiro

Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms Mohsen Bayati, Nima Hamidi, Ramesh Johari, Khashayar Khosravi

Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction Michael Janner, Igor Mordatch, Sergey Levine

Deep Transformers with Latent Depth Xian Li, Asa Cooper Stickland, Yuqing Tang, Xiang Kong

Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows Kunal Gupta, Manmohan Chandraker

Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso Jerome-Alexis Chevalier, Joseph Salmon, Alexandre Gramfort, Bertrand Thirion

A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees Haoran Zhu, Pavankumar Murali, Dzung Phan, Lam Nguyen, Jayant Kalagnanam

Efficient Exact Verification of Binarized Neural Networks Kai Jia, Martin Rinard

Ultra-Low Precision 4-bit Training of Deep Neural Networks Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi (Viji) Srinivasan, Kailash Gopalakrishnan

Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James Kwok, Tong Zhang

On Numerosity of Deep Neural Networks Xi Zhang, Xiaolin Wu

Outlier Robust Mean Estimation with Subgaussian Rates via Stability Ilias Diakonikolas, Daniel M. Kane, Ankit Pensia

Self-Supervised Relationship Probing Jiuxiang Gu, Jason Kuen, Shafiq Joty, Jianfei Cai, Vlad Morariu, Handong Zhao, Tong Sun

Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan Kuruoglu, Yefeng Zheng

Prophet Attention: Predicting Attention with Future Attention Fenglin Liu, Xuancheng Ren, Xian Wu, Shen Ge, Wei Fan, Yuexian Zou, Xu Sun

Language Models are Few-Shot Learners Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei

Margins are Insufficient for Explaining Gradient Boosting Allan Grønlund, Lior Kamma, Kasper Green Larsen

Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics Alex Tseng, Avanti Shrikumar, Anshul Kundaje

MomentumRNN: Integrating Momentum into Recurrent Neural Networks Tan Nguyen, Richard Baraniuk, Andrea Bertozzi, Stanley Osher, Bao Wang

Marginal Utility for Planning in Continuous or Large Discrete Action Spaces Zaheen Ahmad, Levi Lelis, Michael Bowling

Projected Stein Variational Gradient Descent Peng Chen, Omar Ghattas

Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks Mohammadreza Mousavi Kalan, Zalan Fabian, Salman Avestimehr, Mahdi Soltanolkotabi

SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks Fabian Fuchs, Daniel Worrall, Volker Fischer, Max Welling

On the equivalence of molecular graph convolution and molecular wave function with poor basis set Masashi Tsubaki, Teruyasu Mizoguchi

The Power of Predictions in Online Control Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman

Learning Affordance Landscapes for Interaction Exploration in 3D Environments Tushar Nagarajan, Kristen Grauman

Cooperative Multi-player Bandit Optimization Ilai Bistritz, Nicholas Bambos

Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits Shinji Ito, Shuichi Hirahara, Tasuku Soma, Yuichi Yoshida

Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, Dragomir Anguelov

A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model Steffen Czolbe, Oswin Krause, Ingemar Cox, Christian Igel

Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne Haake

Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward Guannan Qu, Yiheng Lin, Adam Wierman, Na Li

Optimizing Neural Networks via Koopman Operator Theory Akshunna S. Dogra, William Redman

SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet

Adversarial Robustness of Supervised Sparse Coding Jeremias Sulam, Ramchandran Muthukumar, Raman Arora

Differentiable Meta-Learning of Bandit Policies Craig Boutilier, Chih-wei Hsu, Branislav Kveton, Martin Mladenov, Csaba Szepesvari, Manzil Zaheer

Biologically Inspired Mechanisms for Adversarial Robustness Manish Reddy Vuyyuru, Andrzej Banburski, Nishka Pant, Tomaso Poggio

Statistical-Query Lower Bounds via Functional Gradients Surbhi Goel, Aravind Gollakota, Adam Klivans

Near-Optimal Reinforcement Learning with Self-Play Yu Bai, Chi Jin, Tiancheng Yu

Network Diffusions via Neural Mean-Field Dynamics Shushan He, Hongyuan Zha, Xiaojing Ye

Self-Distillation as Instance-Specific Label Smoothing Zhilu Zhang, Mert Sabuncu

Towards Problem-dependent Optimal Learning Rates Yunbei Xu, Assaf Zeevi

Cross-lingual Retrieval for Iterative Self-Supervised Training Chau Tran, Yuqing Tang, Xian Li, Jiatao Gu

Rethinking pooling in graph neural networks Diego Mesquita, Amauri Souza, Samuel Kaski

Pointer Graph Networks Petar Veličković, Lars Buesing, Matthew Overlan, Razvan Pascanu, Oriol Vinyals, Charles Blundell

Gradient Regularized V-Learning for Dynamic Treatment Regimes Yao Zhang, Mihaela van der Schaar

Faster Wasserstein Distance Estimation with the Sinkhorn Divergence Lénaïc Chizat, Pierre Roussillon, Flavien Léger, François-Xavier Vialard, Gabriel Peyré

Forethought and Hindsight in Credit Assignment Veronica Chelu, Doina Precup, Hado P. van Hasselt

Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification Hyun-Suk Lee, Yao Zhang, William Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar

Rescuing neural spike train models from bad MLE Diego Arribas, Yuan Zhao, Il Memming Park

Lower Bounds and Optimal Algorithms for Personalized Federated Learning Filip Hanzely, Slavomír Hanzely, Samuel Horváth, Peter Richtarik

Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu

Deep Imitation Learning for Bimanual Robotic Manipulation Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza, Linfeng Zhao, Lawson Wong, Rose Yu

Stationary Activations for Uncertainty Calibration in Deep Learning Lassi Meronen, Christabella Irwanto, Arno Solin

Ensemble Distillation for Robust Model Fusion in Federated Learning Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi

Falcon: Fast Spectral Inference on Encrypted Data Qian Lou, Wen-jie Lu, Cheng Hong, Lei Jiang

On Power Laws in Deep Ensembles Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry P. Vetrov

Practical Quasi-Newton Methods for Training Deep Neural Networks Donald Goldfarb, Yi Ren, Achraf Bahamou

Approximation Based Variance Reduction for Reparameterization Gradients Tomas Geffner, Justin Domke

Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation Jianfeng Zhang, Xuecheng Nie, Jiashi Feng

Consistent feature selection for analytic deep neural networks Vu C. Dinh, Lam S. Ho

Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification Yulin Wang, Kangchen Lv, Rui Huang, Shiji Song, Le Yang, Gao Huang

Information Maximization for Few-Shot Learning Malik Boudiaf, Imtiaz Ziko, Jérôme Rony, Jose Dolz, Pablo Piantanida, Ismail Ben Ayed

Inverse Reinforcement Learning from a Gradient-based Learner Giorgia Ramponi, Gianluca Drappo, Marcello Restelli

Bayesian Multi-type Mean Field Multi-agent Imitation Learning Fan Yang, Alina Vereshchaka, Changyou Chen, Wen Dong

Bayesian Robust Optimization for Imitation Learning Daniel Brown, Scott Niekum, Marek Petrik

Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, Yaron Lipman

Riemannian Continuous Normalizing Flows Emile Mathieu, Maximilian Nickel

Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation Isabella Pozzi, Sander Bohte, Pieter Roelfsema

Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance Ziv Goldfeld, Kristjan Greenewald, Kengo Kato

Online Robust Regression via SGD on the l1 loss Scott Pesme, Nicolas Flammarion

PRANK: motion Prediction based on RANKing Yuriy Biktairov, Maxim Stebelev, Irina Rudenko, Oleh Shliazhko, Boris Yangel

Fighting Copycat Agents in Behavioral Cloning from Observation Histories Chuan Wen, Jierui Lin, Trevor Darrell, Dinesh Jayaraman, Yang Gao

Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model Raphaël Berthier, Francis Bach, Pierre Gaillard

Structured Prediction for Conditional Meta-Learning Ruohan Wang, Yiannis Demiris, Carlo Ciliberto

Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, Dimitris Papailiopoulos

The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, Davide Testuggine

Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function Lingkai Kong, Molei Tao

Identifying Learning Rules From Neural Network Observables Aran Nayebi, Sanjana Srivastava, Surya Ganguli, Daniel L. Yamins

Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Emmanouil Zampetakis

Weakly-Supervised Reinforcement Learning for Controllable Behavior Lisa Lee, Ben Eysenbach, Russ R. Salakhutdinov, Shixiang (Shane) Gu, Chelsea Finn

Improving Policy-Constrained Kidney Exchange via Pre-Screening Duncan McElfresh, Michael Curry, Tuomas Sandholm, John Dickerson

Learning abstract structure for drawing by efficient motor program induction Lucas Tian, Kevin Ellis, Marta Kryven, Josh Tenenbaum

Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective Kaixuan Huang, Yuqing Wang, Molei Tao, Tuo Zhao

Dual Instrumental Variable Regression Krikamol Muandet, Arash Mehrjou, Si Kai Lee, Anant Raj

Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes Hao Chen, Lili Zheng, Raed AL Kontar, Garvesh Raskutti

Interventional Few-Shot Learning Zhongqi Yue, Hanwang Zhang, Qianru Sun, Xian-Sheng Hua

Minimax Value Interval for Off-Policy Evaluation and Policy Optimization Nan Jiang, Jiawei Huang

Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning Yifan Hu, Siqi Zhang, Xin Chen, Niao He

ShiftAddNet: A Hardware-Inspired Deep Network Haoran You, Xiaohan Chen, Yongan Zhang, Chaojian Li, Sicheng Li, Zihao Liu, Zhangyang Wang, Yingyan Lin

Network-to-Network Translation with Conditional Invertible Neural Networks Robin Rombach, Patrick Esser, Bjorn Ommer

Intra-Processing Methods for Debiasing Neural Networks Yash Savani, Colin White, Naveen Sundar Govindarajulu

Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong

Model-based Policy Optimization with Unsupervised Model Adaptation Jian Shen, Han Zhao, Weinan Zhang, Yong Yu

Implicit Regularization and Convergence for Weight Normalization Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel Ward, Qiang Liu

Geometric All-way Boolean Tensor Decomposition Changlin Wan, Wennan Chang, Tong Zhao, Sha Cao, Chi Zhang

Modular Meta-Learning with Shrinkage Yutian Chen, Abram L. Friesen, Feryal Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas

A/B Testing in Dense Large-Scale Networks: Design and Inference Preetam Nandy, Kinjal Basu, Shaunak Chatterjee, Ye Tu

What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman, Chiyuan Zhang

Partially View-aligned Clustering Zhenyu Huang, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, Xi Peng

Partial Optimal Tranport with applications on Positive-Unlabeled Learning Laetitia Chapel, Mokhtar Z. Alaya, Gilles Gasso

Toward the Fundamental Limits of Imitation Learning Nived Rajaraman, Lin Yang, Jiantao Jiao, Kannan Ramchandran

Logarithmic Pruning is All You Need Laurent Orseau, Marcus Hutter, Omar Rivasplata

Hold me tight! Influence of discriminative features on deep network boundaries Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi, Pascal Frossard

Learning from Mixtures of Private and Public Populations Raef Bassily, Shay Moran, Anupama Nandi

Adversarial Weight Perturbation Helps Robust Generalization Dongxian Wu, Shu-Tao Xia, Yisen Wang

Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes Yuval Emek, Ron Lavi, Rad Niazadeh, Yangguang Shi

Adversarial Self-Supervised Contrastive Learning Minseon Kim, Jihoon Tack, Sung Ju Hwang

Normalizing Kalman Filters for Multivariate Time Series Analysis Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski

Learning to summarize with human feedback Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul F. Christiano

Fourier Spectrum Discrepancies in Deep Network Generated Images Tarik Dzanic, Karan Shah, Freddie Witherden

Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks Dongqi Han, Erik De Schutter, Sungho Hong

Learning Dynamic Belief Graphs to Generalize on Text-Based Games Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikuláš Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, Will Hamilton

Triple descent and the two kinds of overfitting: where & why do they appear? Stéphane d'Ascoli, Levent Sagun, Giulio Biroli

Multimodal Graph Networks for Compositional Generalization in Visual Question Answering Raeid Saqur, Karthik Narasimhan

Learning Graph Structure With A Finite-State Automaton Layer Daniel Johnson, Hugo Larochelle, Daniel Tarlow

A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions Yulong Lu, Jianfeng Lu

Unsupervised object-centric video generation and decomposition in 3D Paul Henderson, Christoph H. Lampert

Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization Haoliang Li, Yufei Wang, Renjie Wan, Shiqi Wang, Tie-Qiang Li, Alex Kot

Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? Guoqiang Wu, Jun Zhu

A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances Dieqiao Feng, Carla P. Gomes, Bart Selman

Causal analysis of Covid-19 Spread in Germany Atalanti Mastakouri, Bernhard Schölkopf

Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms Thomas Berrett, Cristina Butucea

Adaptive Gradient Quantization for Data-Parallel SGD Fartash Faghri, Iman Tabrizian, Ilia Markov, Dan Alistarh, Daniel M. Roy, Ali Ramezani-Kebrya

Finite Continuum-Armed Bandits Solenne Gaucher

Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies Itai Gat, Idan Schwartz, Alexander Schwing, Tamir Hazan

Compact task representations as a normative model for higher-order brain activity Severin Berger, Christian K. Machens

Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs Edouard Leurent, Odalric-Ambrym Maillard, Denis Efimov

Co-exposure Maximization in Online Social Networks Sijing Tu, Cigdem Aslay, Aristides Gionis

UCLID-Net: Single View Reconstruction in Object Space Benoit Guillard, Edoardo Remelli, Pascal Fua

Reinforcement Learning for Control with Multiple Frequencies Jongmin Lee, Byung-Jun Lee, Kee-Eung Kim

Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová

Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs Naganand Yadati

A Unified View of Label Shift Estimation Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, Zachary Lipton

Optimal Private Median Estimation under Minimal Distributional Assumptions Christos Tzamos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Ilias Zadik

Breaking the Communication-Privacy-Accuracy Trilemma Wei-Ning Chen, Peter Kairouz, Ayfer Ozgur

Audeo: Audio Generation for a Silent Performance Video Kun Su, Xiulong Liu, Eli Shlizerman

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Self-Distillation Amplifies Regularization in Hilbert Space Hossein Mobahi, Mehrdad Farajtabar, Peter Bartlett

Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators Takeshi Teshima, Isao Ishikawa, Koichi Tojo, Kenta Oono, Masahiro Ikeda, Masashi Sugiyama

Community detection using fast low-cardinality semidefinite programming  Po-Wei Wang, J. Zico Kolter

Modeling Noisy Annotations for Crowd Counting Jia Wan, Antoni Chan

An operator view of policy gradient methods Dibya Ghosh, Marlos C. Machado, Nicolas Le Roux

Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases Senthil Purushwalkam, Abhinav Gupta

Online MAP Inference of Determinantal Point Processes Aditya Bhaskara, Amin Karbasi, Silvio Lattanzi, Morteza Zadimoghaddam

Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement Yongqing Liang, Xin Li, Navid Jafari, Jim Chen

Inferring learning rules from animal decision-making Zoe Ashwood, Nicholas A. Roy, Ji Hyun Bak, Jonathan W. Pillow

Input-Aware Dynamic Backdoor Attack Tuan Anh Nguyen, Anh Tran

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Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition Lin Chen, Qian Yu, Hannah Lawrence, Amin Karbasi

Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks Wei-An Lin, Chun Pong Lau, Alexander Levine, Rama Chellappa, Soheil Feizi

Cross-Scale Internal Graph Neural Network for Image Super-Resolution Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, Chen Change Loy

Unsupervised Representation Learning by Invariance Propagation Feng Wang, Huaping Liu, Di Guo, Sun Fuchun

Restoring Negative Information in Few-Shot Object Detection Yukuan Yang, Fangyun Wei, Miaojing Shi, Guoqi Li

Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, Aleksander Madry

Robust Correction of Sampling Bias using Cumulative Distribution Functions Bijan Mazaheri, Siddharth Jain, Jehoshua Bruck

Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach Alireza Fallah, Aryan Mokhtari, Asuman Ozdaglar

Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation Guoliang Kang, Yunchao Wei, Yi Yang, Yueting Zhuang, Alexander Hauptmann

Classification with Valid and Adaptive Coverage Yaniv Romano, Matteo Sesia, Emmanuel Candes

Learning Global Transparent Models consistent with Local Contrastive Explanations Tejaswini Pedapati, Avinash Balakrishnan, Karthikeyan Shanmugam, Amit Dhurandhar

Learning to Approximate a Bregman Divergence Ali Siahkamari, XIDE XIA, Venkatesh Saligrama, David Castañón, Brian Kulis

Diverse Image Captioning with Context-Object Split Latent Spaces Shweta Mahajan, Stefan Roth

Learning Disentangled Representations of Videos with Missing Data Armand Comas, Chi Zhang, Zlatan Feric, Octavia Camps, Rose Yu

Natural Graph Networks Pim de Haan, Taco S. Cohen, Max Welling

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon

Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts Max Ryabinin, Anton Gusev

Bidirectional Convolutional Poisson Gamma Dynamical Systems wenchao chen, Chaojie Wang, Bo Chen, Yicheng Liu, Hao Zhang, Mingyuan Zhou

Deep Reinforcement and InfoMax Learning Bogdan Mazoure, Remi Tachet des Combes, Thang Long Doan, Philip Bachman, R Devon Hjelm

On ranking via sorting by estimated expected utility Clement Calauzenes, Nicolas Usunier

Distribution-free binary classification: prediction sets, confidence intervals and calibration Chirag Gupta, Aleksandr Podkopaev, Aaditya Ramdas

Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow Didrik Nielsen, Ole Winther

Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals Jing Shi, Xuankai Chang, Pengcheng Guo, Shinji Watanabe, Yusuke Fujita, Jiaming Xu, Bo Xu, Lei Xie

Variance reduction for Random Coordinate Descent-Langevin Monte Carlo ZHIYAN DING, Qin Li

Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration Cédric Colas, Tristan Karch, Nicolas Lair, Jean-Michel Dussoux, Clément Moulin-Frier, Peter Dominey, Pierre-Yves Oudeyer

All Word Embeddings from One Embedding Sho Takase, Sosuke Kobayashi

Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm Adil Salim, Peter Richtarik

How to Characterize The Landscape of Overparameterized Convolutional Neural Networks Yihong Gu, Weizhong Zhang, Cong Fang, Jason D. Lee, Tong Zhang

On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples Richard Zhang

Submodular Meta-Learning Arman Adibi, Aryan Mokhtari, Hamed Hassani

Rethinking Pre-training and Self-training Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, Quoc Le

Unsupervised Sound Separation Using Mixture Invariant Training Scott Wisdom, Efthymios Tzinis, Hakan Erdogan, Ron Weiss, Kevin Wilson, John Hershey

Adaptive Discretization for Model-Based Reinforcement Learning Sean Sinclair, Tianyu Wang, Gauri Jain, Siddhartha Banerjee, Christina Yu

CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching Zeping Yu, Wenxin Zheng, Jiaqi Wang, Qiyi Tang, Sen Nie, Shi Wu

On Warm-Starting Neural Network Training Jordan Ash, Ryan P. Adams

DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks Dennis Wei, Tian Gao, Yue Yu

OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification Taewon Jeong, Heeyoung Kim

An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch Siddharth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna, Peter Stone

Learning About Objects by Learning to Interact with Them Martin Lohmann, Jordi Salvador, Aniruddha Kembhavi, Roozbeh Mottaghi

Learning discrete distributions with infinite support Doron Cohen, Aryeh Kontorovich, Geoﬀrey Wolfer

Dissecting Neural ODEs Stefano Massaroli, Michael Poli, Jinkyoo Park, Atsushi Yamashita, Hajime Asama

Teaching a GAN What Not to Learn Siddarth Asokan, Chandra Seelamantula

Counterfactual Data Augmentation using Locally Factored Dynamics Silviu Pitis, Elliot Creager, Animesh Garg

Rethinking Learnable Tree Filter for Generic Feature Transform Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Xiangyu Zhang, Hongbin Sun, Jian Sun, Nanning Zheng

Self-Supervised Relational Reasoning for Representation Learning Massimiliano Patacchiola, Amos J. Storkey

Sufficient dimension reduction for classification using principal optimal transport direction Cheng Meng, Jun Yu, Jingyi Zhang, Ping Ma, Wenxuan Zhong

Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine Jiajin Li, Caihua Chen, Anthony Man-Cho So

Differentially Private Clustering: Tight Approximation Ratios Badih Ghazi, Ravi Kumar, Pasin Manurangsi

On the Power of Louvain in the Stochastic Block Model Vincent Cohen-Addad, Adrian Kosowski, Frederik Mallmann-Trenn, David Saulpic

Fairness with Overlapping Groups; a Probabilistic Perspective Forest Yang, Mouhamadou Cisse, Sanmi Koyejo

AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control Afshin Oroojlooy, Mohammadreza Nazari, Davood Hajinezhad, Jorge Silva

Searching for Low-Bit Weights in Quantized Neural Networks Zhaohui Yang, Yunhe Wang, Kai Han, Chunjing XU, Chao Xu, Dacheng Tao, Chang Xu

Adaptive Reduced Rank Regression Qiong Wu, Felix MF Wong, Yanhua Li, Zhenming Liu, Varun Kanade

From Predictions to Decisions: Using Lookahead Regularization Nir Rosenfeld, Anna Hilgard, Sai Srivatsa Ravindranath, David C. Parkes

Sequential Bayesian Experimental Design with Variable Cost Structure Sue Zheng, David Hayden, Jason Pacheco, John W. Fisher III

Predictive inference is free with the jackknife+-after-bootstrap Byol Kim, Chen Xu, Rina Barber

Counterfactual Predictions under Runtime Confounding Amanda Coston, Edward Kennedy, Alexandra Chouldechova

Learning Loss for Test-Time Augmentation Ildoo Kim, Younghoon Kim, Sungwoong Kim

Balanced Meta-Softmax for Long-Tailed Visual Recognition Jiawei Ren, Cunjun Yu, shunan sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, hongsheng Li

Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization Sreejith Balakrishnan, Quoc Phong Nguyen, Bryan Kian Hsiang Low, Harold Soh

MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning Elise van der Pol, Daniel Worrall, Herke van Hoof, Frans Oliehoek, Max Welling

How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods Jeya Vikranth Jeyakumar, Joseph Noor, Yu-Hsi Cheng, Luis Garcia, Mani Srivastava

On the Error Resistance of Hinge-Loss Minimization Kunal Talwar

Munchausen Reinforcement Learning Nino Vieillard, Olivier Pietquin, Matthieu Geist

Object Goal Navigation using Goal-Oriented Semantic Exploration Devendra Singh Chaplot, Dhiraj Prakashchand Gandhi, Abhinav Gupta, Russ R. Salakhutdinov

Efficient semidefinite-programming-based inference for binary and multi-class MRFs Chirag Pabbaraju, Po-Wei Wang, J. Zico Kolter

Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing Zihang Dai, Guokun Lai, Yiming Yang, Quoc Le

Semantic Visual Navigation by Watching YouTube Videos Matthew Chang, Arjun Gupta, Saurabh Gupta

Heavy-tailed Representations, Text Polarity Classification & Data Augmentation Hamid Jalalzai, Pierre Colombo, Chloé Clavel, Eric Gaussier, Giovanna Varni, Emmanuel Vignon, Anne Sabourin

SuperLoss: A Generic Loss for Robust Curriculum Learning Thibault Castells, Philippe Weinzaepfel, Jerome Revaud

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models Vijil Chenthamarakshan, Payel Das, Samuel Hoffman, Hendrik Strobelt, Inkit Padhi, Kar Wai Lim, Benjamin Hoover, Matteo Manica, Jannis Born, Teodoro Laino, Aleksandra Mojsilovic

Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards Yijie Guo, Jongwook Choi, Marcin Moczulski, Shengyu Feng, Samy Bengio, Mohammad Norouzi, Honglak Lee

Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations Sebastian Farquhar, Lewis Smith, Yarin Gal

Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms Tengyu Xu, Zhe Wang, Yingbin Liang

Learning Differential Equations that are Easy to Solve Jacob Kelly, Jesse Bettencourt, Matthew J. Johnson, David K. Duvenaud

Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, Kunal Talwar

Influence-Augmented Online Planning for Complex Environments Jinke He, Miguel Suau de Castro, Frans Oliehoek

PAC-Bayes Learning Bounds for Sample-Dependent Priors Pranjal Awasthi, Satyen Kale, Stefani Karp, Mehryar Mohri

Reward-rational (implicit) choice: A unifying formalism for reward learning Hong Jun Jeon, Smitha Milli, Anca Dragan

Probabilistic Time Series Forecasting with Shape and Temporal Diversity Vincent LE GUEN, Nicolas THOME

Low Distortion Block-Resampling with Spatially Stochastic Networks Sarah Hong, Martin Arjovsky, Darryl Barnhart, Ian Thompson

Continual Deep Learning by Functional Regularisation of Memorable Past Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Mohammad Emtiyaz Khan

Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning Pan Li, Yanbang Wang, Hongwei Wang, Jure Leskovec

Fast Fourier Convolution Lu Chi, Borui Jiang, Yadong Mu

Unsupervised Learning of Dense Visual Representations Pedro O. O. Pinheiro, Amjad Almahairi, Ryan Benmalek, Florian Golemo, Aaron C. Courville

Higher-Order Certification For Randomized Smoothing Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel

Learning Structured Distributions From Untrusted Batches: Faster and Simpler Sitan Chen, Jerry Li, Ankur Moitra

Hierarchical Quantized Autoencoders Will Williams, Sam Ringer, Tom Ash, David MacLeod, Jamie Dougherty, John Hughes

Diversity can be Transferred: Output Diversification for White- and Black-box Attacks Yusuke Tashiro, Yang Song, Stefano Ermon

POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis Weichao Mao, Kaiqing Zhang, Qiaomin Xie, Tamer Basar

AvE: Assistance via Empowerment Yuqing Du, Stas Tiomkin, Emre Kiciman, Daniel Polani, Pieter Abbeel, Anca Dragan

Variational Policy Gradient Method for Reinforcement Learning with General Utilities Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvari, Mengdi Wang

Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice Rylan Schaeffer, Mikail Khona, Leenoy Meshulam, Brain Laboratory International, Ila Fiete

Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation Uchenna Akujuobi, Jun Chen, Mohamed Elhoseiny, Michael Spranger, Xiangliang Zhang

Efficient Low Rank Gaussian Variational Inference for Neural Networks Marcin Tomczak, Siddharth Swaroop, Richard Turner

Privacy Amplification via Random Check-Ins Borja Balle, Peter Kairouz, Brendan McMahan, Om Thakkar, Abhradeep Guha Thakurta

Probabilistic Circuits for Variational Inference in Discrete Graphical Models Andy Shih, Stefano Ermon

Your Classifier can Secretly Suffice Multi-Source Domain Adaptation Naveen Venkat, Jogendra Nath Kundu, Durgesh Singh, Ambareesh Revanur, Venkatesh Babu R

Labelling unlabelled videos from scratch with multi-modal self-supervision Yuki Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi

A Non-Asymptotic Analysis for Stein Variational Gradient Descent Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton

Robust Meta-learning for Mixed Linear Regression with Small Batches Weihao Kong, Raghav Somani, Sham Kakade, Sewoong Oh

Bayesian Deep Learning and a Probabilistic Perspective of Generalization Andrew G. Wilson, Pavel Izmailov

Unsupervised Learning of Object Landmarks via Self-Training Correspondence Dimitrios Mallis, Enrique Sanchez, Matthew Bell, Georgios Tzimiropoulos

Randomized tests for high-dimensional regression: A more efficient and powerful solution Yue Li, Ilmun Kim, Yuting Wei

Learning Representations from Audio-Visual Spatial Alignment Pedro Morgado, Yi Li, Nuno Nvasconcelos

Generative View Synthesis: From Single-view Semantics to Novel-view Images Tewodros Amberbir Habtegebrial, Varun Jampani, Orazio Gallo, Didier Stricker

Towards More Practical Adversarial Attacks on Graph Neural Networks Jiaqi Ma, Shuangrui Ding, Qiaozhu Mei

Multi-Task Reinforcement Learning with Soft Modularization Ruihan Yang, Huazhe Xu, YI WU, Xiaolong Wang

Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen

On the training dynamics of deep networks with $L_2$ regularization Aitor Lewkowycz, Guy Gur-Ari

Improved Algorithms for Convex-Concave Minimax Optimization Yuanhao Wang, Jian Li

Deep Variational Instance Segmentation Jialin Yuan, Chao Chen, Fuxin Li

Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence Feng Liu, Xiaoming Liu

Deep Multimodal Fusion by Channel Exchanging Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, Junzhou Huang

Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems Mayalen Etcheverry, Clément Moulin-Frier, Pierre-Yves Oudeyer

AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark

Delay and Cooperation in Nonstochastic Linear Bandits Shinji Ito, Daisuke Hatano, Hanna Sumita, Kei Takemura, Takuro Fukunaga, Naonori Kakimura, Ken-Ichi Kawarabayashi

Probabilistic Orientation Estimation with Matrix Fisher Distributions David Mohlin, Josephine Sullivan, Gérald Bianchi

Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons Qianyi Li, Cengiz Pehlevan

Telescoping Density-Ratio Estimation Benjamin Rhodes, Kai Xu, Michael U. Gutmann

Towards Deeper Graph Neural Networks with Differentiable Group Normalization Kaixiong Zhou, Xiao Huang, Yuening Li, Daochen Zha, Rui Chen, Xia Hu

Stochastic Optimization for Performative Prediction Celestine Mendler-Dünner, Juan Perdomo, Tijana Zrnic, Moritz Hardt

Learning Differentiable Programs with Admissible Neural Heuristics Ameesh Shah, Eric Zhan, Jennifer Sun, Abhinav Verma, Yisong Yue, Swarat Chaudhuri

Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method Michal Derezinski, Rajiv Khanna, Michael W. Mahoney

Domain Adaptation as a Problem of Inference on Graphical Models Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, QINGSONG LIU, Clark Glymour

Network size and size of the weights in memorization with two-layers neural networks Sebastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer

Certifying Strategyproof Auction Networks Michael Curry, Ping-yeh Chiang, Tom Goldstein, John Dickerson

Continual Learning of Control Primitives : Skill Discovery via Reset-Games Kelvin Xu, Siddharth Verma, Chelsea Finn, Sergey Levine

HOI Analysis: Integrating and Decomposing Human-Object Interaction Yong-Lu Li, Xinpeng Liu, Xiaoqian Wu, Yizhuo Li, Cewu Lu

Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering Meng Liu, David F. Gleich

Deep Direct Likelihood Knockoffs Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath

Meta-Neighborhoods Siyuan Shan, Yang Li, Junier B. Oliva

Neural Dynamic Policies for End-to-End Sensorimotor Learning Shikhar Bahl, Mustafa Mukadam, Abhinav Gupta, Deepak Pathak

A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons Gabriel Mahuas, Giulio Isacchini, Olivier Marre, Ulisse Ferrari, Thierry Mora

Decision-Making with Auto-Encoding Variational Bayes Romain Lopez, Pierre Boyeau, Nir Yosef, Michael Jordan, Jeffrey Regier

Attribution Preservation in Network Compression for Reliable Network Interpretation Geondo Park, June Yong Yang, Sung Ju Hwang, Eunho Yang

Feature Importance Ranking for Deep Learning Maksymilian Wojtas, Ke Chen

Causal Estimation with Functional Confounders Aahlad Puli, Adler Perotte, Rajesh Ranganath

Model Inversion Networks for Model-Based Optimization Aviral Kumar, Sergey Levine

Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks Umut Simsekli, Ozan Sener, George Deligiannidis, Murat A. Erdogdu

Exact expressions for double descent and implicit regularization via surrogate random design Michal Derezinski, Feynman T. Liang, Michael W. Mahoney

Certifying Confidence via Randomized Smoothing Aounon Kumar, Alexander Levine, Soheil Feizi, Tom Goldstein

Learning Physical Constraints with Neural Projections Shuqi Yang, Xingzhe He, Bo Zhu

Robust Optimization for Fairness with Noisy Protected Groups Serena Wang, Wenshuo Guo, Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, Michael Jordan

Noise-Contrastive Estimation for Multivariate Point Processes Hongyuan Mei, Tom Wan, Jason Eisner

A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling Xiaotie Deng, Ron Lavi, Tao Lin, Qi Qi, Wenwei WANG, Xiang Yan

Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning Chandrashekar Lakshminarayanan, Amit Vikram Singh

Multiscale Deep Equilibrium Models Shaojie Bai, Vladlen Koltun, J. Zico Kolter

Sparse Graphical Memory for Robust Planning Scott Emmons, Ajay Jain, Misha Laskin, Thanard Kurutach, Pieter Abbeel, Deepak Pathak

Second Order PAC-Bayesian Bounds for the Weighted Majority Vote Andres Masegosa, Stephan Lorenzen, Christian Igel, Yevgeny Seldin

Dirichlet Graph Variational Autoencoder Jia Li, Jianwei Yu, Jiajin Li, Honglei Zhang, Kangfei Zhao, Yu Rong, Hong Cheng, Junzhou Huang

Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction Mariya Toneva, Otilia Stretcu, Barnabas Poczos, Leila Wehbe, Tom M. Mitchell

Counterfactual Vision-and-Language Navigation: Unravelling the Unseen Amin Parvaneh, Ehsan Abbasnejad, Damien Teney, Javen Qinfeng Shi, Anton van den Hengel

Robust Quantization: One Model to Rule Them All moran shkolnik, Brian Chmiel, Ron Banner, Gil Shomron, Yury Nahshan, Alex Bronstein, Uri Weiser

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, Aditi Raghunathan, Jonathan Uesato, Rudy R. Bunel, Shreya Shankar, Jacob Steinhardt, Ian Goodfellow, Percy S. Liang, Pushmeet Kohli

Federated Accelerated Stochastic Gradient Descent Honglin Yuan, Tengyu Ma

Robust Density Estimation under Besov IPM Losses Ananya Uppal, Shashank Singh, Barnabas Poczos

An analytic theory of shallow networks dynamics for hinge loss classification Franco Pellegrini, Giulio Biroli

Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm Tianyi Lin, Nhat Ho, Xi Chen, Marco Cuturi, Michael Jordan

Learning to Orient Surfaces by Self-supervised Spherical CNNs Riccardo Spezialetti, Federico Stella, Marlon Marcon, Luciano Silva, Samuele Salti, Luigi Di Stefano

Adam with Bandit Sampling for Deep Learning Rui Liu, Tianyi Wu, Barzan Mozafari

Parabolic Approximation Line Search for DNNs Maximus Mutschler, Andreas Zell

Agnostic Learning of a Single Neuron with Gradient Descent Spencer Frei, Yuan Cao, Quanquan Gu

Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits Pierre Perrault, Etienne Boursier, Michal Valko, Vianney Perchet

Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry Yossi Arjevani, Michael Field

Generative causal explanations of black-box classifiers Matthew O'Shaughnessy, Gregory Canal, Marissa Connor, Christopher Rozell, Mark Davenport

Sub-sampling for Efficient Non-Parametric Bandit Exploration Dorian Baudry, Emilie Kaufmann, Odalric-Ambrym Maillard

Learning under Model Misspecification: Applications to Variational and Ensemble methods Andres Masegosa

Language Through a Prism: A Spectral Approach for Multiscale Language Representations Alex Tamkin, Dan Jurafsky, Noah Goodman

DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich, Andrew Gardner, Andrew Touchet, Wesley Wilkes, Heath Berry, Hai Li

Towards practical differentially private causal graph discovery Lun Wang, Qi Pang, Dawn Song

Independent Policy Gradient Methods for Competitive Reinforcement Learning Constantinos Daskalakis, Dylan J. Foster, Noah Golowich

The Value Equivalence Principle for Model-Based Reinforcement Learning Christopher Grimm, Andre Barreto, Satinder Singh, David Silver

Structured Convolutions for Efficient Neural Network Design Yash Bhalgat, Yizhe Zhang, Jamie Menjay Lin, Fatih Porikli

Latent World Models For Intrinsically Motivated Exploration Aleksandr Ermolov, Nicu Sebe

Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks Jingqiu Ding, Samuel Hopkins, David Steurer

Policy Improvement via Imitation of Multiple Oracles Ching-An Cheng, Andrey Kolobov, Alekh Agarwal

Training Generative Adversarial Networks by Solving Ordinary Differential Equations Chongli Qin, Yan Wu, Jost Tobias Springenberg, Andy Brock, Jeff Donahue, Timothy Lillicrap, Pushmeet Kohli

Learning of Discrete Graphical Models with Neural Networks Abhijith Jayakumar, Andrey Lokhov, Sidhant Misra, Marc Vuffray

RepPoints v2: Verification Meets Regression for Object Detection Yihong Chen, Zheng Zhang, Yue Cao, Liwei Wang, Stephen Lin, Han Hu

Unfolding the Alternating Optimization for Blind Super Resolution zhengxiong luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan

Entrywise convergence of iterative methods for eigenproblems Vasileios Charisopoulos, Austin R. Benson, Anil Damle

Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views Nanbo Li, Cian Eastwood, Robert Fisher

A Catalyst Framework for Minimax Optimization Junchi Yang, Siqi Zhang, Negar Kiyavash, Niao He

Self-supervised Co-Training for Video Representation Learning Tengda Han, Weidi Xie, Andrew Zisserman

Gradient Estimation with Stochastic Softmax Tricks Max Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison

Meta-Learning Requires Meta-Augmentation Janarthanan Rajendran, Alexander Irpan, Eric Jang

SLIP: Learning to predict in unknown dynamical systems with long-term memory Paria Rashidinejad, Jiantao Jiao, Stuart Russell

Improving GAN Training with Probability Ratio Clipping and Sample Reweighting Yue Wu, Pan Zhou, Andrew G. Wilson, Eric Xing, Zhiting Hu

Bayesian Bits: Unifying Quantization and Pruning Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, Max Welling

On Testing of Samplers Kuldeep S Meel, Yash Pralhad Pote, Sourav Chakraborty

Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael Osborne, Frank Wood

MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, Ming Zhou

Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization Yan Yan, Yi Xu, Qihang Lin, Wei Liu, Tianbao Yang

Woodbury Transformations for Deep Generative Flows You Lu, Bert Huang

Graph Contrastive Learning with Augmentations Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

Gradient Surgery for Multi-Task Learning Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn

Bayesian Probabilistic Numerical Integration with Tree-Based Models Harrison Zhu, Xing Liu, Ruya Kang, Zhichao Shen, Seth Flaxman, Francois-Xavier Briol

Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli

Graph Meta Learning via Local Subgraphs Kexin Huang, Marinka Zitnik

Stochastic Deep Gaussian Processes over Graphs Naiqi Li, Wenjie Li, Jifeng Sun, Yinghua Gao, Yong Jiang, Shu-Tao Xia

Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks Junsouk Choi, Robert Chapkin, Yang Ni

Evaluating Attribution for Graph Neural Networks Benjamin Sanchez-Lengeling, Jennifer Wei, Brian Lee, Emily Reif, Peter Wang, Wesley Qian, Kevin McCloskey, Lucy Colwell , Alexander Wiltschko

On Second Order Behaviour in Augmented Neural ODEs Alexander Norcliffe, Cristian Bodnar, Ben Day, Nikola Simidjievski, Pietro Lió

Neuron Shapley: Discovering the Responsible Neurons Amirata Ghorbani, James Y. Zou

Stochastic Normalizing Flows Hao Wu, Jonas Köhler, Frank Noe

GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification John T. Halloran, David M. Rocke

Random Reshuffling is Not Always Better Christopher M. De Sa

Model Agnostic Multilevel Explanations Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar

NeuMiss networks: differentiable programming for supervised learning with missing values. Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gael Varoquaux

Revisiting Parameter Sharing for Automatic Neural Channel Number Search Jiaxing Wang, Haoli Bai, Jiaxiang Wu, Xupeng Shi, Junzhou Huang, Irwin King, Michael Lyu, Jian Cheng

Differentially-Private Federated Linear Bandits Abhimanyu Dubey, Alex `Sandy' Pentland

Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? Qiwen Cui, Lin Yang

Learning Physical Graph Representations from Visual Scenes Daniel Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li F. Fei-Fei, Jiajun Wu, Josh Tenenbaum, Daniel L. Yamins

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking Anqi Wu, Estefany Kelly Buchanan, Matthew Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora Angelaki, Andrés Bendesky, The International Brain Laboratory The International Brain Laboratory, John P. Cunningham, Liam Paninski

Meta-learning from Tasks with Heterogeneous Attribute Spaces Tomoharu Iwata, Atsutoshi Kumagai

Estimating decision tree learnability with polylogarithmic sample complexity Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan

Sparse Symplectically Integrated Neural Networks Daniel DiPietro, Shiying Xiong, Bo Zhu

Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision Nicolai Hani, Selim Engin, Jun-Jee Chao, Volkan Isler

Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence Thomas Sutter, Imant Daunhawer, Julia Vogt

Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers Kiwon Um, Robert Brand, Yun (Raymond) Fei, Philipp Holl, Nils Thuerey

Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension Ruosong Wang, Russ R. Salakhutdinov, Lin Yang

Predicting Training Time Without Training Luca Zancato, Alessandro Achille, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto

How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions Michael Tsang, Sirisha Rambhatla, Yan Liu

Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield John Choi, Krishan Kumar, Mohammad Khazali, Katie Wingel, Mahdi Choudhury, Adam S. Charles, Bijan Pesaran

Neurosymbolic Reinforcement Learning with Formally Verified Exploration Greg Anderson, Abhinav Verma, Isil Dillig, Swarat Chaudhuri

Wavelet Flow: Fast Training of High Resolution Normalizing Flows Jason J. Yu, Konstantinos G. Derpanis, Marcus A. Brubaker

Multi-task Batch Reinforcement Learning with Metric Learning Jiachen Li, Quan Vuong, Shuang Liu, Minghua Liu, Kamil Ciosek, Henrik Christensen, Hao Su

On 1/n neural representation and robustness Josue Nassar, Piotr Sokol, Sueyeon Chung, Kenneth D. Harris, Il Memming Park

Boundary thickness and robustness in learning models Yaoqing Yang, Rajiv Khanna, Yaodong Yu, Amir Gholami, Kurt Keutzer, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney

Demixed shared component analysis of neural population data from multiple brain areas Yu Takagi, Steven Kennerley, Jun-ichiro Hirayama, Laurence Hunt

Learning Kernel Tests Without Data Splitting Jonas Kübler, Wittawat Jitkrittum, Bernhard Schölkopf, Krikamol Muandet

Unsupervised Data Augmentation for Consistency Training Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, Quoc Le

Subgroup-based Rank-1 Lattice Quasi-Monte Carlo Yueming LYU, Yuan Yuan, Ivor Tsang

Minibatch vs Local SGD for Heterogeneous Distributed Learning Blake E. Woodworth, Kumar Kshitij Patel, Nati Srebro

Multi-task Causal Learning with Gaussian Processes Virginia Aglietti, Theodoros Damoulas, Mauricio Álvarez, Javier González

Proximity Operator of the Matrix Perspective Function and its Applications Joong-Ho (Johann) Won

Generative 3D Part Assembly via Dynamic Graph Learning jialei huang, Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas J. Guibas, Hao Dong

Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention Ekta Sood, Simon Tannert, Philipp Mueller, Andreas Bulling

The Power of Comparisons for Actively Learning Linear Classifiers Max Hopkins, Daniel Kane, Shachar Lovett

From Boltzmann Machines to Neural Networks and Back Again Surbhi Goel, Adam Klivans, Frederic Koehler

Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality Kwang-Sung Jun, Chicheng Zhang

Pruning neural networks without any data by iteratively conserving synaptic flow Hidenori Tanaka, Daniel Kunin, Daniel L. Yamins, Surya Ganguli

Detecting Interactions from Neural Networks via Topological Analysis Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Ying Shan, Xia Hu

Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems Aman Sinha, Matthew O'Kelly, Russ Tedrake, John C. Duchi

Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations Rohan Paleja, Andrew Silva, Letian Chen, Matthew Gombolay

Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes Mengdi Xu, Wenhao Ding, Jiacheng Zhu, ZUXIN LIU, Baiming Chen, Ding Zhao

Benchmarking Deep Learning Interpretability in Time Series Predictions Aya Abdelsalam Ismail, Mohamed Gunady, Hector Corrada Bravo, Soheil Feizi

Federated Principal Component Analysis Andreas Grammenos, Rodrigo Mendoza Smith, Jon Crowcroft, Cecilia Mascolo

(De)Randomized Smoothing for Certifiable Defense against Patch Attacks Alexander Levine, Soheil Feizi

SMYRF - Efficient Attention using Asymmetric Clustering Giannis Daras, Nikita Kitaev, Augustus Odena, Alexandros G. Dimakis

Introducing Routing Uncertainty in Capsule Networks Fabio De Sousa Ribeiro, Georgios Leontidis, Stefanos Kollias

A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration Kevin Scaman, Ludovic DOS SANTOS, Merwan Barlier, Igor Colin

Hyperparameter Ensembles for Robustness and Uncertainty Quantification Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton

Neutralizing Self-Selection Bias in Sampling for Sortition Bailey Flanigan, Paul Gölz, Anupam Gupta, Ariel D. Procaccia

On the Convergence of Smooth Regularized Approximate Value Iteration Schemes Elena Smirnova, Elvis Dohmatob

Off-Policy Evaluation via the Regularized Lagrangian Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans

The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning Harm Van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar

Neural Power Units Niklas Heim, Tomas Pevny, Vasek Smidl

Towards Scalable Bayesian Learning of Causal DAGs Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto

A Dictionary Approach to Domain-Invariant Learning in Deep Networks Ze Wang, Xiuyuan Cheng, Guillermo Sapiro, Qiang Qiu

Bootstrapping neural processes Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh

Large-Scale Adversarial Training for Vision-and-Language Representation Learning Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, Jingjing Liu

Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations Amit Daniely, Hadas Shacham

Compositional Visual Generation with Energy Based Models Yilun Du, Shuang Li, Igor Mordatch

Factor Graph Grammars David Chiang, Darcey Riley

Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs Nikolaos Karalias, Andreas Loukas

Autoregressive Score Matching Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song, Stefano Ermon

Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization Michal Derezinski, Burak Bartan, Mert Pilanci, Michael W. Mahoney

Neural Controlled Differential Equations for Irregular Time Series Patrick Kidger, James Morrill, James Foster, Terry Lyons

On Efficiency in Hierarchical Reinforcement Learning Zheng Wen, Doina Precup, Morteza Ibrahimi, Andre Barreto, Benjamin Van Roy, Satinder Singh

On Correctness of Automatic Differentiation for Non-Differentiable Functions Wonyeol Lee, Hangyeol Yu, Xavier Rival, Hongseok Yang

Probabilistic Linear Solvers for Machine Learning Jonathan Wenger, Philipp Hennig

Dynamic Regret of Policy Optimization in Non-Stationary Environments Yingjie Fei, Zhuoran Yang, Zhaoran Wang, Qiaomin Xie

Multipole Graph Neural Operator for Parametric Partial Differential Equations Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew Stuart, Kaushik Bhattacharya, Anima Anandkumar

BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images Thu H. Nguyen-Phuoc, Christian Richardt, Long Mai, Yongliang Yang, Niloy Mitra

Online Structured Meta-learning Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui (Jessie) Li, Richard Socher, Caiming Xiong

Learning Strategic Network Emergence Games Rakshit Trivedi, Hongyuan Zha

Towards Interpretable Natural Language Understanding with Explanations as Latent Variables Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, Jian Tang

The Mean-Squared Error of Double Q-Learning Wentao Weng, Harsh Gupta, Niao He, Lei Ying, R. Srikant

What Makes for Good Views for Contrastive Learning? Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola

Denoising Diffusion Probabilistic Models Jonathan Ho, Ajay Jain, Pieter Abbeel

Barking up the right tree: an approach to search over molecule synthesis DAGs John Bradshaw, Brooks Paige, Matt J. Kusner, Marwin Segler, José Miguel Hernández-Lobato

On Uniform Convergence and Low-Norm Interpolation Learning Lijia Zhou, Danica J. Sutherland, Nati Srebro

Bandit Samplers for Training Graph Neural Networks Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang, Jun Zhou, Shuang Yang, Le Song, Yuan Qi

Sampling from a k-DPP without looking at all items Daniele Calandriello, Michal Derezinski, Michal Valko

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence Bastian Rieck, Tristan Yates, Christian Bock, Karsten Borgwardt, Guy Wolf, Nicholas Turk-Browne, Smita Krishnaswamy

Hierarchical Poset Decoding for Compositional Generalization in Language Yinuo Guo, Zeqi Lin, Jian-Guang Lou, Dongmei Zhang

Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions Tom Yan, Christian Kroer, Alexander Peysakhovich

Exchangeable Neural ODE for Set Modeling Yang Li, Haidong Yi, Christopher Bender, Siyuan Shan, Junier B. Oliva

Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions Yi Hao, Alon Orlitsky

CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection Qijian Zhang, Runmin Cong, Junhui Hou, Chongyi Li, Yao Zhao

Regularized linear autoencoders recover the principal components, eventually Xuchan Bao, James Lucas, Sushant Sachdeva, Roger B. Grosse

Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization Wei Wang, Min-Ling Zhang

GramGAN: Deep 3D Texture Synthesis From 2D Exemplars Tiziano Portenier, Siavash Arjomand Bigdeli, Orcun Goksel

UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection Yunhang Shen, Rongrong Ji, Zhiwei Chen, Yongjian Wu, Feiyue Huang

Learning Restricted Boltzmann Machines with Sparse Latent Variables Guy Bresler, Rares-Darius Buhai

Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen

Curriculum learning for multilevel budgeted combinatorial problems Adel Nabli, Margarida Carvalho

FedSplit: an algorithmic framework for fast federated optimization Reese Pathak, Martin J. Wainwright

Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data Aude Sportisse, Claire Boyer, Julie Josse

Correlation Robust Influence Maximization Louis Chen, Divya Padmanabhan, Chee Chin Lim, Karthik Natarajan

Neuronal Gaussian Process Regression Johannes Friedrich

Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model Jiaxi Ying, José Vinícius de Miranda Cardoso , Daniel Palomar

Synthetic Data Generators -- Sequential and Private Olivier Bousquet, Roi Livni, Shay Moran

Uncertainty Quantification for Inferring Hawkes Networks Haoyun Wang, Liyan Xie, Alex Cuozzo, Simon Mak, Yao Xie

Implicit Distributional Reinforcement Learning Yuguang Yue, Zhendong Wang, Mingyuan Zhou

Auxiliary Task Reweighting for Minimum-data Learning Baifeng Shi, Judy Hoffman, Kate Saenko, Trevor Darrell, Huijuan Xu

Small Nash Equilibrium Certificates in Very Large Games Brian Zhang, Tuomas Sandholm

Training Linear Finite-State Machines Arash Ardakani, Amir Ardakani, Warren Gross

Efficient active learning of sparse halfspaces with arbitrary bounded noise Chicheng Zhang, Jie Shen, Pranjal Awasthi

Swapping Autoencoder for Deep Image Manipulation Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei Efros, Richard Zhang

Self-Supervised Few-Shot Learning on Point Clouds Charu Sharma, Manohar Kaul

Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC Arun Ganesh, Kunal Talwar

Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE Ding Zhou, Xue-Xin Wei

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez, Konrad Zolna, Rishabh Agarwal, Josh S. Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas

Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, Masashi Sugiyama

Interior Point Solving for LP-based prediction+optimisation Jayanta Mandi, Tias Guns

A simple normative network approximates local non-Hebbian learning in the cortex Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan Sengupta, Dmitri Chklovskii

Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks Roman Pogodin, Peter Latham

Understanding the Role of Training Regimes in Continual Learning Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, Hassan Ghasemzadeh

Fair regression with Wasserstein barycenters Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil

Training Stronger Baselines for Learning to Optimize Tianlong Chen, Weiyi Zhang, Zhou Jingyang, Shiyu Chang, Sijia Liu, Lisa Amini, Zhangyang Wang

Exactly Computing the Local Lipschitz Constant of ReLU Networks Matt Jordan, Alexandros G. Dimakis

Strictly Batch Imitation Learning by Energy-based Distribution Matching Daniel Jarrett, Ioana Bica, Mihaela van der Schaar

On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu

A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems Jiawei Zhang, Peijun Xiao, Ruoyu Sun, Zhiquan Luo

Generating Correct Answers for Progressive Matrices Intelligence Tests Niv Pekar, Yaniv Benny, Lior Wolf

HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, Krystian Mikolajczyk

Preference learning along multiple criteria: A game-theoretic perspective Kush Bhatia, Ashwin Pananjady, Peter Bartlett, Anca Dragan, Martin J. Wainwright

Multi-Plane Program Induction with 3D Box Priors Yikai Li, Jiayuan Mao, Xiuming Zhang, Bill Freeman, Josh Tenenbaum, Noah Snavely, Jiajun Wu

Online Neural Connectivity Estimation with Noisy Group Testing Anne Draelos, John Pearson

Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free Haotao Wang, Tianlong Chen, Shupeng Gui, TingKuei Hu, Ji Liu, Zhangyang Wang

Implicit Neural Representations with Periodic Activation Functions Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, Gordon Wetzstein

Rotated Binary Neural Network Mingbao Lin, Rongrong Ji, Zihan Xu, Baochang Zhang, Yan Wang, Yongjian Wu, Feiyue Huang, Chia-Wen Lin

Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay

Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax Weiss, Balaji Lakshminarayanan

Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment Govinda Kamath, Tavor Baharav, Ilan Shomorony

Hierarchical nucleation in deep neural networks Diego Doimo, Aldo Glielmo, Alessio Ansuini, Alessandro Laio

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, Ren Ng

Graph Geometry Interaction Learning Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, Bin Wang

Differentiable Augmentation for Data-Efficient GAN Training Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han

Heuristic Domain Adaptation Shuhao Cui, Xuan Jin, Shuhui Wang, Yuan He, Qingming Huang

Learning Certified Individually Fair Representations Anian Ruoss, Mislav Balunovic, Marc Fischer, Martin Vechev

Part-dependent Label Noise: Towards Instance-dependent Label Noise Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama

Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, H. Vincent Poor

An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods Yanli Liu, Kaiqing Zhang, Tamer Basar, Wotao Yin

Geometric Exploration for Online Control Orestis Plevrakis, Elad Hazan

Automatic Curriculum Learning through Value Disagreement Yunzhi Zhang, Pieter Abbeel, Lerrel Pinto

MRI Banding Removal via Adversarial Training Aaron Defazio, Tullie Murrell, Michael Recht

The NetHack Learning Environment Heinrich Küttler, Nantas Nardelli, Alexander Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel

Language and Visual Entity Relationship Graph for Agent Navigation Yicong Hong, Cristian Rodriguez, Yuankai Qi, Qi Wu, Stephen Gould

ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping Cher Bass, Mariana da Silva, Carole Sudre, Petru-Daniel Tudosiu, Stephen Smith, Emma Robinson

Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks Zhou Fan, Zhichao Wang

No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium Andrea Celli, Alberto Marchesi, Gabriele Farina, Nicola Gatti

Estimating weighted areas under the ROC curve Andreas Maurer, Massimiliano Pontil

Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study Assaf Dauber, Meir Feder, Tomer Koren, Roi Livni

Generalized Hindsight for Reinforcement Learning Alexander Li, Lerrel Pinto, Pieter Abbeel

Critic Regularized Regression Ziyu Wang, Alexander Novikov, Konrad Zolna, Josh S. Merel, Jost Tobias Springenberg, Scott E. Reed, Bobak Shahriari, Noah Siegel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas

Boosting Adversarial Training with Hypersphere Embedding Tianyu Pang, Xiao Yang, Yinpeng Dong, Kun Xu, Jun Zhu, Hang Su

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra

Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows Ruizhi Deng, Bo Chang, Marcus A. Brubaker, Greg Mori, Andreas Lehrmann

Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent Dimitris Fotakis, Thanasis Lianeas, Georgios Piliouras, Stratis Skoulakis

Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification Lynton Ardizzone, Radek Mackowiak, Carsten Rother, Ullrich Köthe

Detecting Hands and Recognizing Physical Contact in the Wild Supreeth Narasimhaswamy, Trung Nguyen, Minh Hoai Nguyen

On the Theory of Transfer Learning: The Importance of Task Diversity Nilesh Tripuraneni, Michael Jordan, Chi Jin

Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards Vrettos Moulos

Neural Star Domain as Primitive Representation Yuki Kawana, Yusuke Mukuta, Tatsuya Harada

Off-Policy Interval Estimation with Lipschitz Value Iteration Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu

Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics Minhae Kwon, Saurabh Daptardar, Paul R. Schrater, Xaq Pitkow

Deep Statistical Solvers Balthazar Donon, Zhengying Liu, Wenzhuo LIU, Isabelle Guyon, Antoine Marot, Marc Schoenauer

Distributionally Robust Parametric Maximum Likelihood Estimation Viet Anh Nguyen, Xuhui Zhang, Jose Blanchet, Angelos Georghiou

Secretary and Online Matching Problems with Machine Learned Advice Antonios Antoniadis, Themis Gouleakis, Pieter Kleer, Pavel Kolev

Deep Transformation-Invariant Clustering Tom Monnier, Thibault Groueix, Mathieu Aubry

Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree Peizhong Ju, Xiaojun Lin, Jia Liu

Improving Generalization in Reinforcement Learning with Mixture Regularization KAIXIN WANG, Bingyi Kang, Jie Shao, Jiashi Feng

Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework Wanxin Jin, Zhaoran Wang, Zhuoran Yang, Shaoshuai Mou

Learning from Aggregate Observations Yivan Zhang, Nontawat Charoenphakdee, Zhenguo Wu, Masashi Sugiyama

The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models Yingxiang Yang, Negar Kiyavash, Le Song, Niao He

Subgraph Neural Networks Emily Alsentzer, Samuel Finlayson, Michelle Li, Marinka Zitnik

Demystifying Orthogonal Monte Carlo and Beyond Han Lin, Haoxian Chen, Krzysztof M. Choromanski, Tianyi Zhang, Clement Laroche

Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms Alexander Wei, Fred Zhang

A Scalable Approach for Privacy-Preserving Collaborative Machine Learning Jinhyun So, Basak Guler, Salman Avestimehr

Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search Jaehyeon Kim, Sungwon Kim, Jungil Kong, Sungroh Yoon

Towards Learning Convolutions from Scratch Behnam Neyshabur

Cycle-Contrast for Self-Supervised Video Representation Learning Quan Kong, Wenpeng Wei, Ziwei Deng, Tomoaki Yoshinaga, Tomokazu Murakami

Posterior Re-calibration for Imbalanced Datasets Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, Zsolt Kira

Novelty Search in Representational Space for Sample Efficient Exploration Ruo Yu Tao, Vincent Francois-Lavet, Joelle Pineau

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics Parameswaran Kamalaruban, Yu-Ting Huang, Ya-Ping Hsieh, Paul Rolland, Cheng Shi, Volkan Cevher

Adversarial Blocking Bandits Nicholas Bishop, Hau Chan, Debmalya Mandal, Long Tran-Thanh

Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice Shufan Wang, Jian Li, Shiqiang Wang

Multi-label Contrastive Predictive Coding Jiaming Song, Stefano Ermon

Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud SEOHYUN KIM, JaeYoo Park, Bohyung Han

Learning Invariants through Soft Unification Nuri Cingillioglu, Alessandra Russo

One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn

Variational Bayesian Monte Carlo with Noisy Likelihoods Luigi Acerbi

Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes Zaiwei Chen, Siva Theja Maguluri, Sanjay Shakkottai, Karthikeyan Shanmugam

Self-Supervised Generative Adversarial Compression Chong Yu, Jeff Pool

An efficient nonconvex reformulation of stagewise convex optimization problems Rudy R. Bunel, Oliver Hinder, Srinadh Bhojanapalli, Krishnamurthy Dvijotham

From Finite to Countable-Armed Bandits Anand Kalvit, Assaf Zeevi

Adversarial Distributional Training for Robust Deep Learning Yinpeng Dong, Zhijie Deng, Tianyu Pang, Jun Zhu, Hang Su

Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes Andrew Foong, Wessel Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard Turner

Theory-Inspired Path-Regularized Differential Network Architecture Search Pan Zhou, Caiming Xiong, Richard Socher, Steven Chu Hong Hoi

Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices John C. Duchi, Oliver Hinder, Andrew Naber, Yinyu Ye

Learning the Geometry of Wave-Based Imaging Konik Kothari, Maarten de Hoop, Ivan Dokmanić

Greedy inference with structure-exploiting lazy maps Michael Brennan, Daniele Bigoni, Olivier Zahm, Alessio Spantini, Youssef Marzouk

Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun

Finding the Homology of Decision Boundaries with Active Learning Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy, Visar Berisha

Reinforced Molecular Optimization with Neighborhood-Controlled Grammars Chencheng Xu, Qiao Liu, Minlie Huang, Tao Jiang

Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes Dongsheng Ding, Kaiqing Zhang, Tamer Basar, Mihailo Jovanovic

Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau

Certified Defense to Image Transformations via Randomized Smoothing Marc Fischer, Maximilian Baader, Martin Vechev

Estimation of Skill Distribution from a Tournament Ali Jadbabaie, Anuran Makur, Devavrat Shah

Reparameterizing Mirror Descent as Gradient Descent Ehsan Amid, Manfred K. K. Warmuth

General Control Functions for Causal Effect Estimation from IVs Aahlad Puli, Rajesh Ranganath

Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards Kyungjae Lee, Hongjun Yang, Sungbin Lim, Songhwai Oh

Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks Hongwei Jin, Zhan Shi, Venkata Jaya Shankar Ashish Peruri, Xinhua Zhang

Zero-Resource Knowledge-Grounded Dialogue Generation Linxiao Li, Can Xu, Wei Wu, YUFAN ZHAO, Xueliang Zhao, Chongyang Tao

Targeted Adversarial Perturbations for Monocular Depth Prediction Alex Wong, Safa Cicek, Stefano Soatto

Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties Jakob Lindinger, David Reeb, Christoph Lippert, Barbara Rakitsch

Offline Imitation Learning with a Misspecified Simulator Shengyi Jiang, Jingcheng Pang, Yang Yu

Multi-Fidelity Bayesian Optimization via Deep Neural Networks Shibo Li, Wei Xing, Robert Kirby, Shandian Zhe

PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals Henry Charlesworth, Giovanni Montana

Bad Global Minima Exist and SGD Can Reach Them Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas

Optimal Prediction of the Number of Unseen Species with Multiplicity Yi Hao, Ping Li

Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe Sanghack Lee, Elias Bareinboim

Factor Graph Neural Networks Zhen Zhang, Fan Wu, Wee Sun Lee

A Closer Look at Accuracy vs. Robustness Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Russ R. Salakhutdinov, Kamalika Chaudhuri

Curriculum Learning by Dynamic Instance Hardness Tianyi Zhou, Shengjie Wang, Jeffrey Bilmes

Spin-Weighted Spherical CNNs Carlos Esteves, Ameesh Makadia, Kostas Daniilidis

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow

AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference Qian Lou, Song Bian, Lei Jiang

Baxter Permutation Process Masahiro Nakano, Akisato Kimura, Takeshi Yamada, Naonori Ueda

Characterizing emergent representations in a space of candidate learning rules for deep networks Yinan Cao, Christopher Summerfield, Andrew Saxe

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation Rasool Fakoor, Jonas W. Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola

Adaptive Probing Policies for Shortest Path Routing Aditya Bhaskara, Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala

Approximate Heavily-Constrained Learning with Lagrange Multiplier Models Harikrishna Narasimhan, Andrew Cotter, Yichen Zhou, Serena Wang, Wenshuo Guo

Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs Agniva Chowdhury, Palma London, Haim Avron, Petros Drineas

Sliding Window Algorithms for k-Clustering Problems Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning Ximeng Sun, Rameswar Panda, Rogerio Feris, Kate Saenko

Approximate Cross-Validation for Structured Models Soumya Ghosh, Will Stephenson, Tin D. Nguyen, Sameer Deshpande, Tamara Broderick

Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation Sajad Norouzi, David J. Fleet, Mohammad Norouzi

Debiased Contrastive Learning Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, Stefanie Jegelka

UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree Kacper Kania, Maciej Zieba, Tomasz Kajdanowicz

Generalized Boosting Arun Suggala, Bingbin Liu, Pradeep Ravikumar

COT-GAN: Generating Sequential Data via Causal Optimal Transport Tianlin Xu, Li Kevin Wenliang, Michael Munn, Beatrice Acciaio

Impossibility Results for Grammar-Compressed Linear Algebra Amir Abboud, Arturs Backurs, Karl Bringmann, Marvin Künnemann

Understanding spiking networks through convex optimization Allan Mancoo, Sander Keemink, Christian K. Machens

Better Full-Matrix Regret via Parameter-Free Online Learning Ashok Cutkosky

Large-Scale Methods for Distributionally Robust Optimization Daniel Levy, Yair Carmon, John C. Duchi, Aaron Sidford

Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring Taira Tsuchiya, Junya Honda, Masashi Sugiyama

Bandit Linear Control Asaf Cassel, Tomer Koren

Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals Tongzhou Mu, Jiayuan Gu, Zhiwei Jia, Hao Tang, Hao Su

PEP: Parameter Ensembling by Perturbation Alireza Mehrtash, Purang Abolmaesumi, Polina Golland, Tina Kapur, Demian Wassermann, William Wells

Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View Christos Thrampoulidis, Samet Oymak, Mahdi Soltanolkotabi

Adversarial Example Games Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, Will Hamilton

Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts Guilin Li, Junlei Zhang, Yunhe Wang, Chuanjian Liu, Matthias Tan, Yunfeng Lin, Wei Zhang, Jiashi Feng, Tong Zhang

Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach Luofeng Liao, You-Lin Chen, Zhuoran Yang, Bo Dai, Mladen Kolar, Zhaoran Wang

Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms Pinar Ozisik, Philip S. Thomas

Learning to Play Sequential Games versus Unknown Opponents Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause

Further Analysis of Outlier Detection with Deep Generative Models Ziyu Wang, Bin Dai, David Wipf, Jun Zhu

Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning Guangxiang Zhu, Minghao Zhang, Honglak Lee, Chongjie Zhang

Neural Networks Learning and Memorization with (almost) no Over-Parameterization Amit Daniely

Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits Arya Akhavan, Massimiliano Pontil, Alexandre Tsybakov

Towards a Combinatorial Characterization of Bounded-Memory Learning Alon Gonen, Shachar Lovett, Michal Moshkovitz

Chaos, Extremism and Optimism: Volume Analysis of Learning in Games Yun Kuen Cheung, Georgios Piliouras

On Regret with Multiple Best Arms Yinglun Zhu, Robert Nowak

Matrix Completion with Hierarchical Graph Side Information Adel Elmahdy, Junhyung Ahn, Changho Suh, Soheil Mohajer

Is Long Horizon RL More Difficult Than Short Horizon RL? Ruosong Wang, Simon S. Du, Lin Yang, Sham Kakade

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond Charles Margossian, Aki Vehtari, Daniel Simpson, Raj Agrawal

Adversarial Learning for Robust Deep Clustering Xu Yang, Cheng Deng, Kun Wei, Junchi Yan, Wei Liu

Learning Mutational Semantics Brian Hie, Ellen Zhong, Bryan Bryson, Bonnie Berger

Learning to Learn Variational Semantic Memory Xiantong Zhen, Yingjun Du, Huan Xiong, Qiang Qiu, Cees Snoek, Ling Shao

Myersonian Regression Allen Liu, Renato Leme, Jon Schneider

Learnability with Indirect Supervision Signals Kaifu Wang, Qiang Ning, Dan Roth

Towards Safe Policy Improvement for Non-Stationary MDPs Yash Chandak, Scott Jordan, Georgios Theocharous, Martha White, Philip S. Thomas

Finer Metagenomic Reconstruction via Biodiversity Optimization Simon Foucart, David Koslicki

Causal Discovery in Physical Systems from Videos Yunzhu Li, Antonio Torralba, Anima Anandkumar, Dieter Fox, Animesh Garg

Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data Qian Lou, Bo Feng, Geoffrey Charles Fox, Lei Jiang

Smoothed Analysis of Online and Differentially Private Learning Nika Haghtalab, Tim Roughgarden, Abhishek Shetty

Self-Paced Deep Reinforcement Learning Pascal Klink, Carlo D'Eramo, Jan R. Peters, Joni Pajarinen

Kalman Filtering Attention for User Behavior Modeling in CTR Prediction Hu Liu, Jing LU, Xiwei Zhao, Sulong Xu, Hao Peng, Yutong Liu, Zehua Zhang, Jian Li, Junsheng Jin, Yongjun Bao, Weipeng Yan

Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples Jay Nandy, Wynne Hsu, Mong Li Lee

Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels Yi Zhou, Chenglei Wu, Zimo Li, Chen Cao, Yuting Ye, Jason Saragih, Hao Li, Yaser Sheikh

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks Xiang Zhang, Marinka Zitnik

Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction Tong He, John Collomosse, Hailin Jin, Stefano Soatto

Optimal visual search based on a model of target detectability in natural images Shima Rashidi, Krista Ehinger, Andrew Turpin, Lars Kulik

Towards Convergence Rate Analysis of Random Forests for Classification Wei Gao, Zhi-Hua Zhou

List-Decodable Mean Estimation via Iterative Multi-Filtering Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard

Exact Recovery of Mangled Clusters with Same-Cluster Queries Marco Bressan, Nicolò Cesa-Bianchi, Silvio Lattanzi, Andrea Paudice

Steady State Analysis of Episodic Reinforcement Learning Huang Bojun

Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures Julien Launay, Iacopo Poli, François Boniface, Florent Krzakala

Bayesian Optimization for Iterative Learning Vu Nguyen, Sebastian Schulze, Michael Osborne

Minimax Bounds for Generalized Linear Models Kuan-Yun Lee, Thomas Courtade

Projection Robust Wasserstein Distance and Riemannian Optimization Tianyi Lin, Chenyou Fan, Nhat Ho, Marco Cuturi, Michael Jordan

CoinDICE: Off-Policy Confidence Interval Estimation Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvari, Dale Schuurmans

Simple and Fast Algorithm for Binary Integer and Online Linear Programming Xiaocheng Li, Chunlin Sun, Yinyu Ye

Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction Yaodong Yu, Kwan Ho Ryan Chan, Chong You, Chaobing Song, Yi Ma

Learning Rich Rankings Arjun Seshadri, Stephen Ragain, Johan Ugander

Color Visual Illusions: A Statistics-based Computational Model Elad Hirsch, Ayellet Tal

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela

Universal guarantees for decision tree induction via a higher-order splitting criterion Guy Blanc, Neha Gupta, Jane Lange, Li-Yang Tan

Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation Han Zhao, Jianfeng Chi, Yuan Tian, Geoffrey J. Gordon

A Boolean Task Algebra for Reinforcement Learning Geraud Nangue Tasse, Steven James, Benjamin Rosman

Learning with Differentiable Pertubed Optimizers Quentin Berthet, Mathieu Blondel, Olivier Teboul, Marco Cuturi, Jean-Philippe Vert, Francis Bach

Optimal Learning from Verified Training Data Nicholas Bishop, Long Tran-Thanh, Enrico Gerding

Online Linear Optimization with Many Hints Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification Francesca Mignacco, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová

Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim

Exploiting the Surrogate Gap in Online Multiclass Classification Dirk van der Hoeven

The Pitfalls of Simplicity Bias in Neural Networks Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, Praneeth Netrapalli

Automatically Learning Compact Quality-aware Surrogates for Optimization Problems Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe

Empirical Likelihood for Contextual Bandits Nikos Karampatziakis, John Langford, Paul Mineiro

Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver? Vitaly Kurin, Saad Godil, Shimon Whiteson, Bryan Catanzaro

Non-reversible Gaussian processes for identifying latent dynamical structure in neural data Virginia Rutten, Alberto Bernacchia, Maneesh Sahani, Guillaume Hennequin

Listening to Sounds of Silence for Speech Denoising Ruilin Xu, Rundi Wu, Yuko Ishiwaka, Carl Vondrick, Changxi Zheng

BoxE: A Box Embedding Model for Knowledge Base Completion Ralph Abboud, Ismail Ceylan, Thomas Lukasiewicz, Tommaso Salvatori

Coherent Hierarchical Multi-Label Classification Networks Eleonora Giunchiglia, Thomas Lukasiewicz

Walsh-Hadamard Variational Inference for Bayesian Deep Learning Simone Rossi, Sebastien Marmin, Maurizio Filippone

Federated Bayesian Optimization via Thompson Sampling Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet

MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation Saim Wani, Shivansh Patel, Unnat Jain, Angel Chang, Manolis Savva

Neural Complexity Measures Yoonho Lee, Juho Lee, Sung Ju Hwang, Eunho Yang, Seungjin Choi

Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci

Provably adaptive reinforcement learning in metric spaces Tongyi Cao, Akshay Krishnamurthy

ShapeFlow: Learnable Deformation Flows Among 3D Shapes Chiyu Jiang, Jingwei Huang, Andrea Tagliasacchi, Leonidas J. Guibas

Self-Supervised Learning by Cross-Modal Audio-Video Clustering Humam Alwassel, Dhruv Mahajan, Bruno Korbar, Lorenzo Torresani, Bernard Ghanem, Du Tran

Optimal Query Complexity of Secure Stochastic Convex Optimization Wei Tang, Chien-Ju Ho, Yang Liu

DynaBERT: Dynamic BERT with Adaptive Width and Depth Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu

Generalization Bound of Gradient Descent for Non-Convex Metric Learning MINGZHI DONG, Xiaochen Yang, Rui Zhu, Yujiang Wang, Jing-Hao Xue

Dynamic Submodular Maximization Morteza Monemizadeh

Inference for Batched Bandits Kelly Zhang, Lucas Janson, Susan Murphy

Approximate Cross-Validation with Low-Rank Data in High Dimensions Will Stephenson, Madeleine Udell, Tamara Broderick

GANSpace: Discovering Interpretable GAN Controls Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, Sylvain Paris

Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization Samuel Daulton, Maximilian Balandat, Eytan Bakshy

Neuron-level Structured Pruning using Polarization Regularizer Tao Zhuang, Zhixuan Zhang, Yuheng Huang, Xiaoyi Zeng, Kai Shuang, Xiang Li

Limits on Testing Structural Changes in Ising Models Aditya Gangrade, Bobak Nazer, Venkatesh Saligrama

Field-wise Learning for Multi-field Categorical Data Zhibin Li, Jian Zhang, Yongshun Gong, Yazhou Yao, Qiang Wu

Continual Learning in Low-rank Orthogonal Subspaces Arslan Chaudhry, Naeemullah Khan, Puneet Dokania, Philip Torr

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin

Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite

Learning Deformable Tetrahedral Meshes for 3D Reconstruction Jun Gao, Wenzheng Chen, Tommy Xiang, Alec Jacobson, Morgan McGuire, Sanja Fidler

Information theoretic limits of learning a sparse rule Clément Luneau, jean barbier, Nicolas Macris

Self-supervised learning through the eyes of a child Emin Orhan, Vaibhav Gupta, Brenden M. Lake

Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning Tao Han, Junyu Gao, Yuan Yuan, Qi Wang

A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning Arnu Pretorius, Scott Cameron, Elan van Biljon, Thomas Makkink, Shahil Mawjee, Jeremy du Plessis, Jonathan Shock, Alexandre Laterre, Karim Beguir

What shapes feature representations? Exploring datasets, architectures, and training Katherine Hermann, Andrew Lampinen

Optimal Best-arm Identification in Linear Bandits Yassir Jedra, Alexandre Proutiere

Data Diversification: A Simple Strategy For Neural Machine Translation Xuan-Phi Nguyen, Shafiq Joty, Kui Wu, Ai Ti Aw

Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding Yongqi Zhang, Quanming Yao, Lei Chen

CoSE: Compositional Stroke Embeddings Emre Aksan, Thomas Deselaers, Andrea Tagliasacchi, Otmar Hilliges

Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks Jing Xu, Fangwei Zhong, Yizhou Wang

Biological credit assignment through dynamic inversion of feedforward networks Bill Podlaski, Christian K. Machens

Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching Di Hu, Rui Qian, Minyue Jiang, Xiao Tan, Shilei Wen, Errui Ding, Weiyao Lin, Dejing Dou

Learning Multi-Agent Communication through Structured Attentive Reasoning Murtaza Rangwala, Ryan Williams

Private Identity Testing for High-Dimensional Distributions Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan Ullman, Lydia Zakynthinou

On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression Denny Wu, Ji Xu

An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search Kyunghyun Lee, Byeong-Uk Lee, Ukcheol Shin, In So Kweon

MetaSDF: Meta-Learning Signed Distance Functions Vincent Sitzmann, Eric Chan, Richard Tucker, Noah Snavely, Gordon Wetzstein

Simple and Scalable Sparse k-means Clustering via Feature Ranking Zhiyue Zhang, Kenneth Lange, Jason Xu

Model-based Adversarial Meta-Reinforcement Learning Zichuan Lin, Garrett Thomas, Guangwen Yang, Tengyu Ma

Graph Policy Network for Transferable Active Learning on Graphs Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, Jian Tang

Towards a Better Global Loss Landscape of GANs Ruoyu Sun, Tiantian Fang, Alexander Schwing

Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning Tabish Rashid, Gregory Farquhar, Bei Peng, Shimon Whiteson

BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits Mo Tiwari, Martin J. Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony

UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging Chaoning Zhang, Philipp Benz, Adil Karjauv, Geng Sun, In So Kweon

Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders Masha Itkina, Boris Ivanovic, Ransalu Senanayake, Mykel J. Kochenderfer, Marco Pavone

An Unbiased Risk Estimator for Learning with Augmented Classes Yu-Jie Zhang, Peng Zhao, Lanjihong Ma, Zhi-Hua Zhou

AutoBSS: An Efficient Algorithm for Block Stacking Style Search Yikang Zhang, Jian Zhang, Zhao Zhong

Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point Bita Darvish Rouhani, Daniel Lo, Ritchie Zhao, Ming Liu, Jeremy Fowers, Kalin Ovtcharov , Anna Vinogradsky, Sarah Massengill , Lita Yang, Ray Bittner, Alessandro Forin, Haishan Zhu, Taesik Na, Prerak Patel, Shuai Che, Lok Chand Koppaka , XIA SONG, Subhojit Som, Kaustav Das, Saurabh T, Steve Reinhardt , Sitaram Lanka, Eric Chung, Doug Burger

Stochastic Optimization with Laggard Data Pipelines Naman Agarwal, Rohan Anil, Tomer Koren, Kunal Talwar, Cyril Zhang

Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs Dasol Hwang, Jinyoung Park, Sunyoung Kwon, KyungMin Kim, Jung-Woo Ha, Hyunwoo J. Kim

GPS-Net: Graph-based Photometric Stereo Network Zhuokun Yao, Kun Li, Ying Fu, Haofeng Hu, Boxin Shi

Consistent Structural Relation Learning for Zero-Shot Segmentation Peike Li, Yunchao Wei, Yi Yang

Model Selection in Contextual Stochastic Bandit Problems Aldo Pacchiano, My Phan, Yasin Abbasi Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvari

Truncated Linear Regression in High Dimensions Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis

Incorporating Pragmatic Reasoning Communication into Emergent Language Yipeng Kang, Tonghan Wang, Gerard de Melo

Deep Subspace Clustering with Data Augmentation Mahdi Abavisani, Alireza Naghizadeh, Dimitris Metaxas, Vishal Patel

An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits Julian Katz-Samuels, Lalit Jain, zohar karnin, Kevin G. Jamieson

Can Graph Neural Networks Count Substructures? Zhengdao Chen, Lei Chen, Soledad Villar, Joan Bruna

A Bayesian Perspective on Training Speed and Model Selection Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, Mark van der Wilk

On the Modularity of Hypernetworks Tomer Galanti, Lior Wolf

Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies Nathan Kallus, Masatoshi Uehara

Provably Efficient Neural GTD for Off-Policy Learning Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong

Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, Dale Schuurmans

Stable and expressive recurrent vision models Drew Linsley, Alekh Karkada Ashok, Lakshmi Narasimhan Govindarajan, Rex Liu, Thomas Serre

Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form Hicham Janati, Boris Muzellec, Gabriel Peyré, Marco Cuturi

BRP-NAS: Prediction-based NAS using GCNs Lukasz Dudziak, Thomas Chau, Mohamed Abdelfattah, Royson Lee, Hyeji Kim, Nicholas Lane

Deep Shells: Unsupervised Shape Correspondence with Optimal Transport Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé, Daniel Cremers

ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding Yibo Yang, Hongyang Li, Shan You, Fei Wang, Chen Qian, Zhouchen Lin

Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D Ankit Goyal, Kaiyu Yang, Dawei Yang, Jia Deng

Regularizing Black-box Models for Improved Interpretability Gregory Plumb, Maruan Al-Shedivat, Ángel Alexander Cabrera, Adam Perer, Eric Xing, Ameet Talwalkar

Trust the Model When It Is Confident: Masked Model-based Actor-Critic Feiyang Pan, Jia He, Dandan Tu, Qing He

Semi-Supervised Neural Architecture Search Renqian Luo, Xu Tan, Rui Wang, Tao Qin, Enhong Chen, Tie-Yan Liu

Consistency Regularization for Certified Robustness of Smoothed Classifiers Jongheon Jeong, Jinwoo Shin

Robust Multi-Agent Reinforcement Learning with Model Uncertainty Kaiqing Zhang, TAO SUN, Yunzhe Tao, Sahika Genc, Sunil Mallya, Tamer Basar

SIRI: Spatial Relation Induced Network For Spatial Description Resolution peiyao wang, Weixin Luo, Yanyu Xu, Haojie Li, Shugong Xu, Jianyu Yang, Shenghua Gao

Adaptive Shrinkage Estimation for Streaming Graphs Nesreen Ahmed, Nick Duffield

Make One-Shot Video Object Segmentation Efficient Again Tim Meinhardt, Laura Leal-Taixé

Depth Uncertainty in Neural Networks Javier Antoran, James Allingham, José Miguel Hernández-Lobato

Non-Euclidean Universal Approximation Anastasis Kratsios, Ievgen Bilokopytov

Constraining Variational Inference with Geometric Jensen-Shannon Divergence Jacob Deasy, Nikola Simidjievski, Pietro Lió

Gibbs Sampling with People Peter Harrison, Raja Marjieh, Federico Adolfi, Pol van Rijn, Manuel Anglada-Tort, Ofer Tchernichovski, Pauline Larrouy-Maestri, Nori Jacoby

HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory Jie Ren, Minjia Zhang, Dong Li

FrugalML: How to use ML Prediction APIs more accurately and cheaply Lingjiao Chen, Matei Zaharia, James Y. Zou

Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth Guy Bresler, Dheeraj Nagaraj

Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning Filippos Christianos, Lukas Schäfer, Stefano Albrecht

Monotone operator equilibrium networks Ezra Winston, J. Zico Kolter

When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar

Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control Yaofeng Desmond Zhong, Naomi Leonard

High-Dimensional Sparse Linear Bandits Botao Hao, Tor Lattimore, Mengdi Wang

Non-Stochastic Control with Bandit Feedback Paula Gradu, John Hallman, Elad Hazan

Generalized Leverage Score Sampling for Neural Networks Jason D. Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, zheng Yu

An Optimal Elimination Algorithm for Learning a Best Arm Avinatan Hassidim, Ron Kupfer, Yaron Singer

Efficient Projection-free Algorithms for Saddle Point Problems Cheng Chen, Luo Luo, Weinan Zhang, Yong Yu

A mathematical model for automatic differentiation in machine learning Jérôme Bolte, Edouard Pauwels

Unsupervised Text Generation by Learning from Search Jingjing Li, Zichao Li, Lili Mou, Xin Jiang, Michael Lyu, Irwin King

Learning Compositional Rules via Neural Program Synthesis Maxwell Nye, Armando Solar-Lezama, Josh Tenenbaum, Brenden M. Lake

Incorporating BERT into Parallel Sequence Decoding with Adapters Junliang Guo, Zhirui Zhang, Linli Xu, Hao-Ran Wei, Boxing Chen, Enhong Chen

Estimating Fluctuations in Neural Representations of Uncertain Environments Sahand Farhoodi, Mark Plitt, Lisa Giocomo, Uri Eden

Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation KwanYong Park, Sanghyun Woo, Inkyu Shin, In So Kweon

SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm Yi Hao, Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar

Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks Ryo Karakida, Kazuki Osawa

General Transportability of Soft Interventions: Completeness Results Juan Correa, Elias Bareinboim

GAIT-prop: A biologically plausible learning rule derived from backpropagation of error Nasir Ahmad, Marcel A. J. van Gerven, Luca Ambrogioni

Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing Vishaal Krishnan, Abed AlRahman Al Makdah, Fabio Pasqualetti

SCOP: Scientific Control for Reliable Neural Network Pruning Yehui Tang, Yunhe Wang, Yixing Xu, Dacheng Tao, Chunjing XU, Chao Xu, Chang Xu

Provably Consistent Partial-Label Learning Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama

Robust, Accurate Stochastic Optimization for Variational Inference Akash Kumar Dhaka, Alejandro Catalina, Michael R. Andersen, Måns Magnusson, Jonathan Huggins, Aki Vehtari

Discovering conflicting groups in signed networks Ruo-Chun Tzeng, Bruno Ordozgoiti, Aristides Gionis

Learning Some Popular Gaussian Graphical Models without Condition Number Bounds Jonathan Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra

Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding Victor Veitch, Anisha Zaveri

Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions Matthew Faw, Rajat Sen, Karthikeyan Shanmugam, Constantine Caramanis, Sanjay Shakkottai

Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition Ben Adlam, Jeffrey Pennington

VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar

The Smoothed Possibility of Social Choice Lirong Xia

A Decentralized Parallel Algorithm for Training Generative Adversarial Nets Mingrui Liu, Wei Zhang, Youssef Mroueh, Xiaodong Cui, Jarret Ross, Tianbao Yang, Payel Das

Phase retrieval in high dimensions: Statistical and computational phase transitions Antoine Maillard, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

Fair Performance Metric Elicitation Gaurush Hiranandani, Harikrishna Narasimhan, Sanmi Koyejo

Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function Quoc Tran Dinh, Deyi Liu, Lam Nguyen

Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information Genevieve Flaspohler, Nicholas A. Roy, John W. Fisher III

Soft Contrastive Learning for Visual Localization Janine Thoma, Danda Pani Paudel, Luc V. Gool

Fine-Grained Dynamic Head for Object Detection Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Hongbin Sun, Jian Sun, Nanning Zheng

LoCo: Local Contrastive Representation Learning Yuwen Xiong, Mengye Ren, Raquel Urtasun

Modeling and Optimization Trade-off in Meta-learning Katelyn Gao, Ozan Sener

SnapBoost: A Heterogeneous Boosting Machine Thomas Parnell, Andreea Anghel, Małgorzata Łazuka, Nikolas Ioannou, Sebastian Kurella, Peshal Agarwal, Nikolaos Papandreou, Haralampos Pozidis

On Adaptive Distance Estimation Yeshwanth Cherapanamjeri, Jelani Nelson

Stage-wise Conservative Linear Bandits Ahmadreza Moradipari, Christos Thrampoulidis, Mahnoosh Alizadeh

RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces Sebastien Ehrhardt, Oliver Groth, Aron Monszpart, Martin Engelcke, Ingmar Posner, Niloy Mitra, Andrea Vedaldi

Metric-Free Individual Fairness in Online Learning Yahav Bechavod, Christopher Jung, Steven Z. Wu

GreedyFool: Distortion-Aware Sparse Adversarial Attack Xiaoyi Dong, Dongdong Chen, Jianmin Bao, Chuan Qin, Lu Yuan, Weiming Zhang, Nenghai Yu, Dong Chen

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data Chao Ma, Sebastian Tschiatschek, Richard Turner, José Miguel Hernández-Lobato, Cheng Zhang

RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist Chaochao Yan, Qianggang Ding, Peilin Zhao, Shuangjia Zheng, JINYU YANG, Yang Yu, Junzhou Huang

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining Austin Tripp, Erik Daxberger, José Miguel Hernández-Lobato

Improved Sample Complexity for Incremental Autonomous Exploration in MDPs Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning Han Cai, Chuang Gan, Ligeng Zhu, Song Han

RD$^2$: Reward Decomposition with Representation Decomposition Zichuan Lin, Derek Yang, Li Zhao, Tao Qin, Guangwen Yang, Tie-Yan Liu

Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID Yixiao Ge, Feng Zhu, Dapeng Chen, Rui Zhao, hongsheng Li

Fairness constraints can help exact inference in structured prediction Kevin Bello, Jean Honorio

Instance-based Generalization in Reinforcement Learning Martin Bertran, Natalia Martinez, Mariano Phielipp, Guillermo Sapiro

Smooth And Consistent Probabilistic Regression Trees Sami Alkhoury, Emilie Devijver, Marianne Clausel, Myriam Tami, Eric Gaussier, georges Oppenheim

Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming Vo Nguyen Le Duy, Hiroki Toda, Ryota Sugiyama, Ichiro Takeuchi

Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses Ronald (James) Cotton, Fabian Sinz, Andreas Tolias

Winning the Lottery with Continuous Sparsification Pedro Savarese, Hugo Silva, Michael Maire

Adversarial robustness via robust low rank representations Pranjal Awasthi, Himanshu Jain, Ankit Singh Rawat, Aravindan Vijayaraghavan

Joints in Random Forests Alvaro Correia, Robert Peharz, Cassio P. de Campos

Compositional Generalization by Learning Analytical Expressions Qian Liu, Shengnan An, Jian-Guang Lou, Bei Chen, Zeqi Lin, Yan Gao, Bin Zhou, Nanning Zheng, Dongmei Zhang

JAX MD: A Framework for Differentiable Physics Samuel Schoenholz, Ekin Dogus Cubuk

An implicit function learning approach for parametric modal regression Yangchen Pan, Ehsan Imani, Amir-massoud Farahmand, Martha White

SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images Chen-Hsuan Lin, Chaoyang Wang, Simon Lucey

Coresets for Robust Training of Deep Neural Networks against Noisy Labels Baharan Mirzasoleiman, Kaidi Cao, Jure Leskovec

Adapting to Misspecification in Contextual Bandits Dylan J. Foster, Claudio Gentile, Mehryar Mohri, Julian Zimmert

Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters Kaiyi Ji, Jason D. Lee, Yingbin Liang, H. Vincent Poor

MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures Jeong Un Ryu, JaeWoong Shin, Hae Beom Lee, Sung Ju Hwang

Learning to solve TV regularised problems with unrolled algorithms Hamza Cherkaoui, Jeremias Sulam, Thomas Moreau

Object-Centric Learning with Slot Attention Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, Thomas Kipf

Improving robustness against common corruptions by covariate shift adaptation Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge

Deep Smoothing of the Implied Volatility Surface Damien Ackerer, Natasa Tagasovska, Thibault Vatter

Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck

Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning Sirisha Rambhatla, Xingguo Li, Jarvis Haupt

Look-ahead Meta Learning for Continual Learning Gunshi Gupta, Karmesh Yadav, Liam Paull

A polynomial-time algorithm for learning nonparametric causal graphs Ming Gao, Yi Ding, Bryon Aragam

Sparse Learning with CART Jason Klusowski

Proximal Mapping for Deep Regularization Mao Li, Yingyi Ma, Xinhua Zhang

Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models Andrew Jesson, Sören Mindermann, Uri Shalit, Yarin Gal

Hierarchical Granularity Transfer Learning Shaobo Min, Hongtao Xie, Hantao Yao, Xuran Deng, Zheng-Jun Zha, Yongdong Zhang

Deep active inference agents using Monte-Carlo methods Zafeirios Fountas, Noor Sajid, Pedro Mediano, Karl Friston

Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations Alexander Ritchie, Robert A. Vandermeulen, Clayton Scott

Manifold structure in graph embeddings Patrick Rubin-Delanchy

Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web Zhenwei Dai, Anshumali Shrivastava

MCUNet: Tiny Deep Learning on IoT Devices Ji Lin, Wei-Ming Chen, Yujun Lin, john cohn, Chuang Gan, Song Han

In search of robust measures of generalization Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy

Task-agnostic Exploration in Reinforcement Learning Xuezhou Zhang, Yuzhe Ma, Adish Singla

Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery Yingjie Wang, Hong Chen, Feng Zheng, Chen Xu, Tieliang Gong, Yanhong Chen

Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration Andrea Zanette, Alessandro Lazaric, Mykel J. Kochenderfer, Emma Brunskill

Softmax Deep Double Deterministic Policy Gradients Ling Pan, Qingpeng Cai, Longbo Huang

Online Decision Based Visual Tracking via Reinforcement Learning ke Song, Wei Zhang, Ran Song, Yibin Li

Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity Gonçalo Correia, Vlad Niculae, Wilker Aziz, André Martins

DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs yaxing wang, Lu Yu, Joost van de Weijer

Distributional Robustness with IPMs and links to Regularization and GANs Hisham Husain

A shooting formulation of deep learning François-Xavier Vialard, Roland Kwitt, Susan Wei, Marc Niethammer

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances Jihoon Tack, Sangwoo Mo, Jongheon Jeong, Jinwoo Shin

Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning Meng Zhou, Ziyu Liu, Pengwei Sui, Yixuan Li, Yuk Ying Chung

MATE: Plugging in Model Awareness to Task Embedding for Meta Learning Xiaohan Chen, Zhangyang Wang, Siyu Tang, Krikamol Muandet

Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits Siwei Wang, Longbo Huang, John C. S. Lui

Predictive Information Accelerates Learning in RL Kuang-Huei Lee, Ian Fischer, Anthony Liu, Yijie Guo, Honglak Lee, John Canny, Sergio Guadarrama

Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization Sam Hopkins, Jerry Li, Fred Zhang

High-Fidelity Generative Image Compression Fabian Mentzer, George D. Toderici, Michael Tschannen, Eirikur Agustsson

A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning Bhavya Kailkhura, Jayaraman Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, Timo Bremer

Counterexample-Guided Learning of Monotonic Neural Networks Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein, Guy Van den Broeck

A Novel Approach for Constrained Optimization in Graphical Models Sara Rouhani, Tahrima Rahman, Vibhav Gogate

Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology Quynh N. Nguyen, Marco Mondelli

On the Trade-off between Adversarial and Backdoor Robustness Cheng-Hsin Weng, Yan-Ting Lee, Shan-Hung (Brandon) Wu

Implicit Graph Neural Networks Fangda Gu, Heng Chang, Wenwu Zhu, Somayeh Sojoudi, Laurent El Ghaoui

Rethinking Importance Weighting for Deep Learning under Distribution Shift Tongtong Fang, Nan Lu, Gang Niu, Masashi Sugiyama

Guiding Deep Molecular Optimization with Genetic Exploration Sungsoo Ahn, Junsu Kim, Hankook Lee, Jinwoo Shin

Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks Wenrui Zhang, Peng Li

TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation DONGXU LI, Chenchen Xu, Xin Yu, Kaihao Zhang, Benjamin Swift, Hanna Suominen, Hongdong Li

Neural Topographic Factor Analysis for fMRI Data Eli Sennesh, Zulqarnain Khan, Yiyu Wang, J Benjamin Hutchinson, Ajay Satpute, Jennifer Dy, Jan-Willem van de Meent

Neural Architecture Generator Optimization Robin Ru, Pedro Esperança, Fabio Maria Carlucci

A Bandit Learning Algorithm and Applications to Auction Design Kim Thang Nguyen

MetaPoison: Practical General-purpose Clean-label Data Poisoning W. Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, Tom Goldstein

Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation Devavrat Shah, Dogyoon Song, Zhi Xu, Yuzhe Yang

Training Generative Adversarial Networks with Limited Data Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, Timo Aila

Deeply Learned Spectral Total Variation Decomposition Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola Schoenlieb

FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training Yonggan Fu, Haoran You, Yang Zhao, Yue Wang, Chaojian Li, Kailash Gopalakrishnan, Zhangyang Wang, Yingyan Lin

Improving Neural Network Training in Low Dimensional Random Bases Frithjof Gressmann, Zach Eaton-Rosen, Carlo Luschi

Safe Reinforcement Learning via Curriculum Induction Matteo Turchetta, Andrey Kolobov, Shital Shah, Andreas Krause, Alekh Agarwal

Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning Nino Vieillard, Tadashi Kozuno, Bruno Scherrer, Olivier Pietquin, Remi Munos, Matthieu Geist

How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? Mrinank Sharma, Sören Mindermann, Jan Brauner, Gavin Leech, Anna Stephenson, Tomáš Gavenčiak, Jan Kulveit, Yee Whye Teh, Leonid Chindelevitch, Yarin Gal

Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses Kaivalya Rawal, Himabindu Lakkaraju

Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization Benjamin Aubin, Florent Krzakala, Yue Lu, Lenka Zdeborová

Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method Kiran K. Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh

PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks Minh Vu, My T. Thai

Few-Cost Salient Object Detection with Adversarial-Paced Learning Dingwen Zhang, HaiBin Tian, Jungong Han

Minimax Estimation of Conditional Moment Models Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis

Causal Imitation Learning With Unobserved Confounders Junzhe Zhang, Daniel Kumor, Elias Bareinboim

Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling Tong Che, Ruixiang ZHANG, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio

Learning Black-Box Attackers with Transferable Priors and Query Feedback Jiancheng YANG, Yangzhou Jiang, Xiaoyang Huang, Bingbing Ni, Chenglong Zhao

Locally Differentially Private (Contextual) Bandits Learning Kai Zheng, Tianle Cai, Weiran Huang, Zhenguo Li, Liwei Wang

Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham

Kernel Based Progressive Distillation for Adder Neural Networks Yixing Xu, Chang Xu, Xinghao Chen, Wei Zhang, Chunjing XU, Yunhe Wang

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization Paul Barde, Julien Roy, Wonseok Jeon, Joelle Pineau, Chris Pal, Derek Nowrouzezahrai

Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space Shangchen Du, Shan You, Xiaojie Li, Jianlong Wu, Fei Wang, Chen Qian, Changshui Zhang

The Wasserstein Proximal Gradient Algorithm Adil Salim, Anna Korba, Giulia Luise

Universally Quantized Neural Compression Eirikur Agustsson, Lucas Theis

Temporal Variability in Implicit Online Learning Nicolò Campolongo, Francesco Orabona

Investigating Gender Bias in Language Models Using Causal Mediation Analysis Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, Stuart Shieber

Off-Policy Imitation Learning from Observations Zhuangdi Zhu, Kaixiang Lin, Bo Dai, Jiayu Zhou

Escaping Saddle-Point Faster under Interpolation-like Conditions Abhishek Roy, Krishnakumar Balasubramanian, Saeed Ghadimi, Prasant Mohapatra

Matérn Gaussian Processes on Riemannian Manifolds Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth (he/him)

Improved Techniques for Training Score-Based Generative Models Yang Song, Stefano Ermon

wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations Alexei Baevski, Yuhao Zhou, Abdelrahman Mohamed, Michael Auli

A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs Nevena Lazic, Dong Yin, Mehrdad Farajtabar, Nir Levine, Dilan Gorur, Chris Harris, Dale Schuurmans

Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients William Moses, Valentin Churavy

Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, Mi Zhang

Value-driven Hindsight Modelling Arthur Guez, Fabio Viola, Theophane Weber, Lars Buesing, Steven Kapturowski, Doina Precup, David Silver, Nicolas Heess

Dynamic Regret of Convex and Smooth Functions Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou

On Convergence of Nearest Neighbor Classifiers over Feature Transformations Luka Rimanic, Cedric Renggli, Bo Li, Ce Zhang

Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments Steven Jecmen, Hanrui Zhang, Ryan Liu, Nihar Shah, Vincent Conitzer, Fei Fang

Contrastive learning of global and local features for medical image segmentation with limited annotations Krishna Chaitanya, Ertunc Erdil, Neerav Karani, Ender Konukoglu

Self-Supervised Graph Transformer on Large-Scale Molecular Data Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying WEI, Wenbing Huang, Junzhou Huang

Generative Neurosymbolic Machines Jindong Jiang, Sungjin Ahn

How many samples is a good initial point worth in Low-rank Matrix Recovery? Jialun Zhang, Richard Zhang

CSER: Communication-efficient SGD with Error Reset Cong Xie, Shuai Zheng, Sanmi Koyejo, Indranil Gupta, Mu Li, Haibin Lin

Efficient estimation of neural tuning during naturalistic behavior Edoardo Balzani, Kaushik Lakshminarasimhan, Dora Angelaki, Cristina Savin

High-recall causal discovery for autocorrelated time series with latent confounders Andreas Gerhardus, Jakob Runge

Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes Juan Luis GonzalezBello, Munchurl Kim

Joint Contrastive Learning with Infinite Possibilities Qi Cai, Yu Wang, Yingwei Pan, Ting Yao, Tao Mei

Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time Jerry Li, Guanghao Ye

Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models Adarsh Keshav Jeewajee, Leslie Kaelbling

GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators Dingfan Chen, Tribhuvanesh Orekondy, Mario Fritz

SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling

Learning Causal Effects via Weighted Empirical Risk Minimization Yonghan Jung, Jin Tian, Elias Bareinboim

Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes Minh Hoang, Nghia Hoang, Hai Pham, David Woodruff

Incorporating Interpretable Output Constraints in Bayesian Neural Networks Wanqian Yang, Lars Lorch, Moritz Graule, Himabindu Lakkaraju, Finale Doshi-Velez

Multi-Stage Influence Function Hongge Chen, Si Si, Yang Li, Ciprian Chelba, Sanjiv Kumar, Duane Boning, Cho-Jui Hsieh

Probabilistic Fair Clustering Seyed Esmaeili, Brian Brubach, Leonidas Tsepenekas, John Dickerson

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty Miguel Monteiro, Loic Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA Ilyes Khemakhem, Ricardo Monti, Diederik Kingma, Aapo Hyvarinen

Testing Determinantal Point Processes Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka

CogLTX: Applying BERT to Long Texts Ming Ding, Chang Zhou, Hongxia Yang, Jie Tang

f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning Xin Zhang, Yanhua Li, Ziming Zhang, Zhi-Li Zhang

Non-parametric Models for Non-negative Functions Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi

Uncertainty Aware Semi-Supervised Learning on Graph Data Xujiang Zhao, Feng Chen, Shu Hu, Jin-Hee Cho

ConvBERT: Improving BERT with Span-based Dynamic Convolution Zi-Hang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan

Practical No-box Adversarial Attacks against DNNs Qizhang Li, Yiwen Guo, Hao Chen

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model Gen Li, Yuting Wei, Yuejie Chi, Yuantao Gu, Yuxin Chen

Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization Hassan Mortagy, Swati Gupta, Sebastian Pokutta

Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks Alexander Shekhovtsov, Viktor Yanush, Boris Flach

Reward Propagation Using Graph Convolutional Networks Martin Klissarov, Doina Precup

LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll

Fully Dynamic Algorithm for Constrained Submodular Optimization Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakub M. Tarnawski, Morteza Zadimoghaddam

Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation Yogesh Balaji, Rama Chellappa, Soheil Feizi

Autofocused oracles for model-based design Clara Fannjiang, Jennifer Listgarten

Debiasing Averaged Stochastic Gradient Descent to handle missing values Aude Sportisse, Claire Boyer, Aymeric Dieuleveut, Julie Josse

Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning Younggyo Seo, Kimin Lee, Ignasi Clavera Gilaberte, Thanard Kurutach, Jinwoo Shin, Pieter Abbeel

CompRess: Self-Supervised Learning by Compressing Representations Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash

Sample complexity and effective dimension for regression on manifolds Andrew McRae, Justin Romberg, Mark Davenport

The phase diagram of approximation rates for deep neural networks Dmitry Yarotsky, Anton Zhevnerchuk

Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network Lifeng Shen, Zhuocong Li, James Kwok

EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints Sachin Chauhan, Kashish Bansal, Rijurekha Sen

Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN Tao Fang, Yu Qi, Gang Pan

Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, Sergey Levine

A Spectral Energy Distance for Parallel Speech Synthesis Alexey Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner

Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations Joel Dapello, Tiago Marques, Martin Schrimpf, Franziska Geiger, David Cox, James J. DiCarlo

Learning from Positive and Unlabeled Data with Arbitrary Positive Shift Zayd Hammoudeh, Daniel Lowd

Deep Energy-based Modeling of Discrete-Time Physics Takashi Matsubara, Ai Ishikawa, Takaharu Yaguchi

Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning Iro Laina, Ruth Fong, Andrea Vedaldi

Self-Learning Transformations for Improving Gaze and Head Redirection Yufeng Zheng, Seonwook Park, Xucong Zhang, Shalini De Mello, Otmar Hilliges

Language-Conditioned Imitation Learning for Robot Manipulation Tasks Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Stefan Lee, Chitta Baral, Heni Ben Amor

POMDPs in Continuous Time and Discrete Spaces Bastian Alt, Matthias Schultheis, Heinz Koeppl

Exemplar Guided Active Learning Jason S. Hartford, Kevin Leyton-Brown, Hadas Raviv, Dan Padnos, Shahar Lev, Barak Lenz

Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps Chaozheng Wu, Jian Chen, Qiaoyu Cao, Jianchi Zhang, Yunxin Tai, Lin Sun, Kui Jia

Node Embeddings and Exact Low-Rank Representations of Complex Networks Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis

Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications Sarah Perrin, Julien Perolat, Mathieu Lauriere, Matthieu Geist, Romuald Elie, Olivier Pietquin

Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction Benoît Colange, Jaakko Peltonen, Michael Aupetit, Denys Dutykh, Sylvain Lespinats

On Infinite-Width Hypernetworks Etai Littwin, Tomer Galanti, Lior Wolf, Greg Yang

Interferobot: aligning an optical interferometer by a reinforcement learning agent Dmitry Sorokin, Alexander Ulanov, Ekaterina Sazhina, Alexander Lvovsky

Program Synthesis with Pragmatic Communication Yewen Pu, Kevin Ellis, Marta Kryven, Josh Tenenbaum, Armando Solar-Lezama

Principal Neighbourhood Aggregation for Graph Nets Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, Petar Veličković

Reliable Graph Neural Networks via Robust Aggregation Simon Geisler, Daniel Zügner, Stephan Günnemann

Instance Selection for GANs Terrance DeVries, Michal Drozdzal, Graham W. Taylor

Linear Disentangled Representations and Unsupervised Action Estimation Matthew Painter, Adam Prugel-Bennett, Jonathon Hare

Video Frame Interpolation without Temporal Priors Youjian Zhang, Chaoyue Wang, Dacheng Tao

Learning compositional functions via multiplicative weight updates Jeremy Bernstein, Jiawei Zhao, Markus Meister, Ming-Yu Liu, Anima Anandkumar, Yisong Yue

Sample Complexity of Uniform Convergence for Multicalibration Eliran Shabat, Lee Cohen, Yishay Mansour

Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement Miao Zhang, Huiqi Li, Shirui Pan, Xiaojun Chang, Zongyuan Ge, Steven Su

The interplay between randomness and structure during learning in RNNs Friedrich Schuessler, Francesca Mastrogiuseppe, Alexis Dubreuil, Srdjan Ostojic, Omri Barak

A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang

Instance-wise Feature Grouping Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter Castaldi, Jennifer Dy

Robust Disentanglement of a Few Factors at a Time using rPU-VAE Benjamin Estermann, Markus Marks, Mehmet Fatih Yanik

PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning Alekh Agarwal, Mikael Henaff, Sham Kakade, Wen Sun

Group Contextual Encoding for 3D Point Clouds Xu Liu, Chengtao Li, Jian Wang, Jingbo Wang, Boxin Shi, Xiaodong He

Latent Bandits Revisited Joey Hong, Branislav Kveton, Manzil Zaheer, Yinlam Chow, Amr Ahmed, Craig Boutilier

Is normalization indispensable for training deep neural network? Jie Shao, Kai Hu, Changhu Wang, Xiangyang Xue, Bhiksha Raj

Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborová

Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks Amir Rahimi, Amirreza Shaban, Ching-An Cheng, Richard Hartley, Byron Boots

Linear Time Sinkhorn Divergences using Positive Features Meyer Scetbon, Marco Cuturi

VarGrad: A Low-Variance Gradient Estimator for Variational Inference Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco Ruiz, Omer Deniz Akyildiz

A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction Ziqi Ke, Haris Vikalo

Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method Qi Zhou, Yufei Kuang, Zherui Qiu, Houqiang Li, Jie Wang

Adversarial Counterfactual Learning and Evaluation for Recommender System Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control Giorgos ('Yorgos') Mamakoukas, Orest Xherija, Todd Murphey

Evolving Normalization-Activation Layers Hanxiao Liu, Andy Brock, Karen Simonyan, Quoc Le

ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi (Viji) Srinivasan, Wei Zhang, Kailash Gopalakrishnan

RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder Cheng Chi, Fangyun Wei, Han Hu

Efficient Learning of Discrete Graphical Models Marc Vuffray, Sidhant Misra, Andrey Lokhov

Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals Ilias Diakonikolas, Daniel Kane, Nikos Zarifis

Neurosymbolic Transformers for Multi-Agent Communication Jeevana Priya Inala, Yichen Yang, James Paulos, Yewen Pu, Osbert Bastani, Vijay Kumar, Martin Rinard, Armando Solar-Lezama

Fairness in Streaming Submodular Maximization: Algorithms and Hardness Marwa El Halabi, Slobodan Mitrović, Ashkan Norouzi-Fard, Jakab Tardos, Jakub M. Tarnawski

Smoothed Geometry for Robust Attribution Zifan Wang, Haofan Wang, Shakul Ramkumar, Piotr Mardziel, Matt Fredrikson, Anupam Datta

Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms Sascha Saralajew, Lars Holdijk, Thomas Villmann

Multi-agent active perception with prediction rewards Mikko Lauri, Frans Oliehoek

A Local Temporal Difference Code for Distributional Reinforcement Learning Pablo Tano, Peter Dayan, Alexandre Pouget

Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions Hayata Yamasaki, Sathyawageeswar Subramanian, Sho Sonoda, Masato Koashi

CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations Davis Rempe, Tolga Birdal, Yongheng Zhao, Zan Gojcic, Srinath Sridhar, Leonidas J. Guibas

Deep Automodulators Ari Heljakka, Yuxin Hou, Juho Kannala, Arno Solin

Convolutional Tensor-Train LSTM for Spatio-Temporal Learning Jiahao Su, Wonmin Byeon, Jean Kossaifi, Furong Huang, Jan Kautz, Anima Anandkumar

The Potts-Ising model for discrete multivariate data Zahra Razaee, Arash Amini

Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech Shailee Jain, Vy Vo, Shivangi Mahto, Amanda LeBel, Javier S. Turek, Alexander Huth

Group-Fair Online Allocation in Continuous Time Semih Cayci, Swati Gupta, Atilla Eryilmaz

Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis Gang Wang, Songtao Lu, Georgios Giannakis, Gerald Tesauro, Jian Sun

Understanding Gradient Clipping in Private SGD: A Geometric Perspective Xiangyi Chen, Steven Z. Wu, Mingyi Hong

O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank Reddi, Sanjiv Kumar

Identifying signal and noise structure in neural population activity with Gaussian process factor models Stephen Keeley, Mikio Aoi, Yiyi Yu, Spencer Smith, Jonathan W. Pillow

Equivariant Networks for Hierarchical Structures Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh

MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics Luca De Gennaro Aquino, Stephan Eckstein

A Discrete Variational Recurrent Topic Model without the Reparametrization Trick Mehdi Rezaee, Francis Ferraro

Transferable Graph Optimizers for ML Compilers Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Wong, Peter Ma, Qiumin Xu, Hanxiao Liu, Phitchaya Phothilimtha, Shen Wang, Anna Goldie, Azalia Mirhoseini, James Laudon

Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces Akash Saha, Balamurugan Palaniappan

Learning Bounds for Risk-sensitive Learning Jaeho Lee, Sejun Park, Jinwoo Shin

Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints Marc Finzi, Ke Alexander Wang, Andrew G. Wilson

Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency Robert Geirhos, Kristof Meding, Felix A. Wichmann

Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael Jordan

Constant-Expansion Suffices for Compressed Sensing with Generative Priors Constantinos Daskalakis, Dhruv Rohatgi, Emmanouil Zampetakis

RANet: Region Attention Network for Semantic Segmentation Dingguo Shen, Yuanfeng Ji, Ping Li, Yi Wang, Di Lin

A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent Zhenyu Liao, Romain Couillet, Michael W. Mahoney

Learning sparse codes from compressed representations with biologically plausible local wiring constraints Kion Fallah, Adam Willats, Ninghao Liu, Christopher Rozell

Self-Imitation Learning via Generalized Lower Bound Q-learning Yunhao Tang

Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia

Directional Pruning of Deep Neural Networks Shih-Kang Chao, Zhanyu Wang, Yue Xing, Guang Cheng

Smoothly Bounding User Contributions in Differential Privacy Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Lijie Ren

Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping Minjia Zhang, Yuxiong He

Online Planning with Lookahead Policies Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor

Learning Deep Attribution Priors Based On Prior Knowledge Ethan Weinberger, Joseph Janizek, Su-In Lee

Using noise to probe recurrent neural network structure and prune synapses Eli Moore, Rishidev Chaudhuri

NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity Sang-gil Lee, Sungwon Kim, Sungroh Yoon

Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge Chaoyang He, Murali Annavaram, Salman Avestimehr

Neural FFTs for Universal Texture Image Synthesis Morteza Mardani, Guilin Liu, Aysegul Dundar, Shiqiu Liu, Andrew Tao, Bryan Catanzaro

Graph Cross Networks with Vertex Infomax Pooling Maosen Li, Siheng Chen, Ya Zhang, Ivor Tsang

Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms Hilal Asi, John C. Duchi

Calibration of Shared Equilibria in General Sum Partially Observable Markov Games Nelson Vadori, Sumitra Ganesh, Prashant Reddy, Manuela Veloso

MOPO: Model-based Offline Policy Optimization Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y. Zou, Sergey Levine, Chelsea Finn, Tengyu Ma

Building powerful and equivariant graph neural networks with structural message-passing Clément Vignac, Andreas Loukas, Pascal Frossard

Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning Sebastian Curi, Felix Berkenkamp, Andreas Krause

Practical Low-Rank Communication Compression in Decentralized Deep Learning Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi

Mutual exclusivity as a challenge for deep neural networks Kanishk Gandhi, Brenden M. Lake

3D Shape Reconstruction from Vision and Touch Edward Smith, Roberto Calandra, Adriana Romero, Georgia Gkioxari, David Meger, Jitendra Malik, Michal Drozdzal

GradAug: A New Regularization Method for Deep Neural Networks Taojiannan Yang, Sijie Zhu, Chen Chen

An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay Scott Fujimoto, David Meger, Doina Precup

Learning Utilities and Equilibria in Non-Truthful Auctions Hu Fu, Tao Lin

Rational neural networks Nicolas Boulle, Yuji Nakatsukasa, Alex Townsend

DISK: Learning local features with policy gradient Michał Tyszkiewicz, Pascal Fua, Eduard Trulls

Transfer Learning via $\ell_1$ Regularization Masaaki Takada, Hironori Fujisawa

GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network Prune Truong, Martin Danelljan, Luc V. Gool, Radu Timofte

Deep Inverse Q-learning with Constraints Gabriel Kalweit, Maria Huegle, Moritz Werling, Joschka Boedecker

Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities Chaobing Song, Zhengyuan Zhou, Yichao Zhou, Yong Jiang, Yi Ma

Prediction with Corrupted Expert Advice Idan Amir, Idan Attias, Tomer Koren, Yishay Mansour, Roi Livni

Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency Fang Zhao, Shengcai Liao, Kaihao Zhang, Ling Shao

Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition Zijun Cui, Tengfei Song, Yuru Wang, Qiang Ji

Point process models for sequence detection in high-dimensional neural spike trains Alex Williams, Anthony Degleris, Yixin Wang, Scott Linderman

Adversarial Attacks on Linear Contextual Bandits Evrard Garcelon, Baptiste Roziere, Laurent Meunier, Jean Tarbouriech, Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta

Meta-Consolidation for Continual Learning Joseph K J, Vineeth N Balasubramanian

Organizing recurrent network dynamics by task-computation to enable continual learning Lea Duncker, Laura Driscoll, Krishna V. Shenoy, Maneesh Sahani, David Sussillo

Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting Jorge Mendez, Boyu Wang, Eric Eaton

Kernel Methods Through the Roof: Handling Billions of Points Efficiently Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, Alessandro Rudi

Spike and slab variational Bayes for high dimensional logistic regression Kolyan Ray, Botond Szabo, Gabriel Clara

Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness Long Zhao, Ting Liu, Xi Peng, Dimitris Metaxas

Fast geometric learning with symbolic matrices Jean Feydy, Alexis Glaunès, Benjamin Charlier, Michael Bronstein

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang

CoinPress: Practical Private Mean and Covariance Estimation Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan Ullman

Planning with General Objective Functions: Going Beyond Total Rewards Ruosong Wang, Peilin Zhong, Simon S. Du, Russ R. Salakhutdinov, Lin Yang

Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks Yimeng Min, Frederik Wenkel, Guy Wolf

KFC: A Scalable Approximation Algorithm for $k$−center Fair Clustering Elfarouk Harb, Ho Shan Lam

Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms Yingying Li, Na Li

Learning the Linear Quadratic Regulator from Nonlinear Observations Zakaria Mhammedi, Dylan J. Foster, Max Simchowitz, Dipendra Misra, Wen Sun, Akshay Krishnamurthy, Alexander Rakhlin, John Langford

Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate Zhiyuan Li, Kaifeng Lyu, Sanjeev Arora

Scalable Graph Neural Networks via Bidirectional Propagation Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, Ji-Rong Wen

Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning Jaehyung Kim, Youngbum Hur, Sejun Park, Eunho Yang, Sung Ju Hwang, Jinwoo Shin

Assisted Learning: A Framework for Multi-Organization Learning Xun Xian, Xinran Wang, Jie Ding, Reza Ghanadan

The Strong Screening Rule for SLOPE Johan Larsson, Malgorzata Bogdan, Jonas Wallin

STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks Meiyi Ma, Ji Gao, Lu Feng, John Stankovic

Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks Jy-yong Sohn, Dong-Jun Han, Beongjun Choi, Jaekyun Moon

Reducing Adversarially Robust Learning to Non-Robust PAC Learning Omar Montasser, Steve Hanneke, Nati Srebro

Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples Samarth Sinha, Zhengli Zhao, Anirudh Goyal ALIAS PARTH GOYAL, Colin A. Raffel, Augustus Odena

Black-Box Optimization with Local Generative Surrogates Sergey Shirobokov, Vladislav Belavin, Michael Kagan, Andrei Ustyuzhanin, Atilim Gunes Baydin

Efficient Generation of Structured Objects with Constrained Adversarial Networks Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, Andrea Passerini

Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning Huan Fu, Shunming Li, Rongfei Jia, Mingming Gong, Binqiang Zhao, Dacheng Tao

Recovery of sparse linear classifiers from mixture of responses Venkata Gandikota, Arya Mazumdar, Soumyabrata Pal

Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S Meel, N. V. Vinodchandran

A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints Omid Sadeghi, Prasanna Raut, Maryam Fazel

Learning Sparse Prototypes for Text Generation Junxian He, Taylor Berg-Kirkpatrick, Graham Neubig

Implicit Rank-Minimizing Autoencoder Li Jing, Jure Zbontar, yann lecun

Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning Jianda Chen, Shangyu Chen, Sinno Jialin Pan

Task-Oriented Feature Distillation Linfeng Zhang, Yukang Shi, Zuoqiang Shi, Kaisheng Ma, Chenglong Bao

Entropic Causal Inference: Identifiability and Finite Sample Results Spencer Compton, Murat Kocaoglu, Kristjan Greenewald, Dmitriy Katz

Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement Ben Eysenbach, XINYANG GENG, Sergey Levine, Russ R. Salakhutdinov

Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis Shaocong Ma, Yi Zhou, Shaofeng Zou

AdaTune: Adaptive Tensor Program Compilation Made Efficient Menghao Li, Minjia Zhang, Chi Wang, Mingqin Li

When Do Neural Networks Outperform Kernel Methods? Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari

STEER : Simple Temporal Regularization For Neural ODE Arnab Ghosh, Harkirat Behl, Emilien Dupont, Philip Torr, Vinay Namboodiri

A Variational Approach for Learning from Positive and Unlabeled Data Hui Chen, Fangqing Liu, Yin Wang, Liyue Zhao, Hao Wu

Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut Shenfei Pei, Feiping Nie, Rong Wang, Xuelong Li

Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations Joshua Glaser, Matthew Whiteway, John P. Cunningham, Liam Paninski, Scott Linderman

Coresets via Bilevel Optimization for Continual Learning and Streaming Zalán Borsos, Mojmir Mutny, Andreas Krause

Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang

Understanding and Exploring the Network with Stochastic Architectures Zhijie Deng, Yinpeng Dong, Shifeng Zhang, Jun Zhu

All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation jean barbier, Nicolas Macris, Cynthia Rush

Deep Evidential Regression Alexander Amini, Wilko Schwarting, Ava Soleimany, Daniela Rus

Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks Randall Balestriero, Sebastien PARIS, Richard Baraniuk

Bayesian Pseudocoresets Dionysis Manousakas, Zuheng Xu, Cecilia Mascolo, Trevor Campbell

See, Hear, Explore: Curiosity via Audio-Visual Association Victoria Dean, Shubham Tulsiani, Abhinav Gupta

Adversarial Training is a Form of Data-dependent Operator Norm Regularization Kevin Roth, Yannic Kilcher, Thomas Hofmann

A Biologically Plausible Neural Network for Slow Feature Analysis David Lipshutz, Charles Windolf, Siavash Golkar, Dmitri Chklovskii

Learning Feature Sparse Principal Subspace Lai Tian, Feiping Nie, Rong Wang, Xuelong Li

Online Adaptation for Consistent Mesh Reconstruction in the Wild Xueting Li, Sifei Liu, Shalini De Mello, Kihwan Kim, Xiaolong Wang, Ming-Hsuan Yang, Jan Kautz

Online learning with dynamics: A minimax perspective Kush Bhatia, Karthik Sridharan

Learning to Select Best Forecast Tasks for Clinical Outcome Prediction Yuan Xue, Nan Du, Anne Mottram, Martin Seneviratne, Andrew M. Dai

Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping Eduard Gorbunov, Marina Danilova, Alexander Gasnikov

Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach Peter W. Glynn, Ramesh Johari, Mohammad Rasouli

From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering Ines Chami, Albert Gu, Vaggos Chatziafratis, Christopher Ré

The Autoencoding Variational Autoencoder Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy Dvijotham, Sven Gowal, Pushmeet Kohli

A Fair Classifier Using Kernel Density Estimation Jaewoong Cho, Gyeongjo Hwang, Changho Suh

A Randomized Algorithm to Reduce the Support of Discrete Measures Francesco Cosentino, Harald Oberhauser, Alessandro Abate

Distributionally Robust Federated Averaging Yuyang Deng, Mohammad Mahdi Kamani, Mehrdad Mahdavi

Sharp uniform convergence bounds through empirical centralization Cyrus Cousins, Matteo Riondato

COBE: Contextualized Object Embeddings from Narrated Instructional Video Gedas Bertasius, Lorenzo Torresani

Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control Zhiyuan Xu, Kun Wu, Zhengping Che, Jian Tang, Jieping Ye

Finite Versus Infinite Neural Networks: an Empirical Study Jaehoon Lee, Samuel Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein

Supermasks in Superposition Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, Ali Farhadi

Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors Jorio Cocola, Paul Hand, Vlad Voroninski

Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition Zihan Zhang, Yuan Zhou, Xiangyang Ji

Learning to Incentivize Other Learning Agents Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha

Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation Jianyuan Wang, Yiran Zhong, Yuchao Dai, Kaihao Zhang, Pan Ji, Hongdong Li

Distributionally Robust Local Non-parametric Conditional Estimation Viet Anh Nguyen, Fan Zhang, Jose Blanchet, Erick Delage, Yinyu Ye

Robust Multi-Object Matching via Iterative Reweighting of the Graph Connection Laplacian Yunpeng Shi, Shaohan Li, Gilad Lerman

Meta-Gradient Reinforcement Learning with an Objective Discovered Online Zhongwen Xu, Hado P. van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver

Learning Strategy-Aware Linear Classifiers Yiling Chen, Yang Liu, Chara Podimata

Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss Shuang Qiu, Xiaohan Wei, Zhuoran Yang, Jieping Ye, Zhaoran Wang

Calibrating Deep Neural Networks using Focal Loss Jishnu Mukhoti, Viveka Kulharia, Amartya Sanyal, Stuart Golodetz, Philip Torr, Puneet Dokania

Optimizing Mode Connectivity via Neuron Alignment Norman Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai

Information Theoretic Regret Bounds for Online Nonlinear Control Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, Wen Sun

A kernel test for quasi-independence Tamara Fernandez, Wenkai Xu, Marc Ditzhaus, Arthur Gretton

First Order Constrained Optimization in Policy Space Yiming Zhang, Quan Vuong, Keith Ross

Learning Augmented Energy Minimization via Speed Scaling Etienne Bamas, Andreas Maggiori, Lars Rohwedder, Ola Svensson

Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning Luca Oneto, Michele Donini, Giulia Luise, Carlo Ciliberto, Andreas Maurer, Massimiliano Pontil

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus

Why are Adaptive Methods Good for Attention Models? Jingzhao Zhang, Sai Praneeth Karimireddy, Andreas Veit, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar, Suvrit Sra

Neural Sparse Representation for Image Restoration Yuchen Fan, Jiahui Yu, Yiqun Mei, Yulun Zhang, Yun Fu, Ding Liu, Thomas S. Huang

Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates Kaiwen Zhou, Anthony Man-Cho So, James Cheng

Robust Sequence Submodular Maximization Gamal Sallam, Zizhan Zheng, Jie Wu, Bo Ji

Certified Monotonic Neural Networks Xingchao Liu, Xing Han, Na Zhang, Qiang Liu

System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina Cornelius Schröder, David Klindt, Sarah Strauss, Katrin Franke, Matthias Bethge, Thomas Euler, Philipp Berens

Efficient Algorithms for Device Placement of DNN Graph Operators Jakub M. Tarnawski, Amar Phanishayee, Nikhil Devanur, Divya Mahajan, Fanny Nina Paravecino

Active Invariant Causal Prediction: Experiment Selection through Stability Juan L. Gamella, Christina Heinze-Deml

BOSS: Bayesian Optimization over String Spaces Henry Moss, David Leslie, Daniel Beck, Javier González, Paul Rayson

Model Interpretability through the lens of Computational Complexity Pablo Barceló, Mikaël Monet, Jorge Pérez, Bernardo Subercaseaux

Markovian Score Climbing: Variational Inference with KL(p||q) Christian Naesseth, Fredrik Lindsten, David Blei

Improved Analysis of Clipping Algorithms for Non-convex Optimization Bohang Zhang, Jikai Jin, Cong Fang, Liwei Wang

Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang

A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan

StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks Guangmo Tong

A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms Donghwan Lee, Niao He

Kernel Alignment Risk Estimator: Risk Prediction from Training Data Arthur Jacot, Berfin Simsek, Francesco Spadaro, Clement Hongler, Franck Gabriel

Calibrating CNNs for Lifelong Learning Pravendra Singh, Vinay Kumar Verma, Pratik Mazumder, Lawrence Carin, Piyush Rai

Online Convex Optimization Over Erdos-Renyi Random Networks Jinlong Lei, Peng Yi, Yiguang Hong, Jie Chen, Guodong Shi

Robustness of Bayesian Neural Networks to Gradient-Based Attacks Ginevra Carbone, Matthew Wicker, Luca Laurenti, Andrea Patane', Luca Bortolussi, Guido Sanguinetti

Parametric Instance Classification for Unsupervised Visual Feature learning Yue Cao, Zhenda Xie, Bin Liu, Yutong Lin, Zheng Zhang, Han Hu

Sparse Weight Activation Training Md Aamir Raihan, Tor Aamodt

Collapsing Bandits and Their Application to Public Health Intervention Aditya Mate, Jackson Killian, Haifeng Xu, Andrew Perrault, Milind Tambe

Neural Sparse Voxel Fields Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, Christian Theobalt

A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding Bruno Lecouat, Jean Ponce, Julien Mairal

The Discrete Gaussian for Differential Privacy Clément L. Canonne, Gautam Kamath, Thomas Steinke

Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing Arun Jambulapati, Jerry Li, Kevin Tian

Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes Ayoub El Hanchi, David Stephens

Learning efficient task-dependent representations with synaptic plasticity Colin Bredenberg, Eero Simoncelli, Cristina Savin

A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions Wei Deng, Guang Lin, Faming Liang

Error Bounds of Imitating Policies and Environments Tian Xu, Ziniu Li, Yang Yu

Disentangling Human Error from Ground Truth in Segmentation of Medical Images Le Zhang, Ryutaro Tanno, Mou-Cheng Xu, Chen Jin, Joseph Jacob, Olga Cicarrelli, Frederik Barkhof, Daniel Alexander

Consequences of Misaligned AI Simon Zhuang, Dylan Hadfield-Menell

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Julien Roy, Paul Barde, Félix Harvey, Derek Nowrouzezahrai, Chris Pal

Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences Bowen Baker

Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics Aranyak Mehta, Uri Nadav, Alexandros Psomas, Aviad Rubinstein

Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs Hao Tang, Zhiao Huang, Jiayuan Gu, Bao-Liang Lu, Hao Su

Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses Yihan Zhou, Victor Sanches Portella, Mark Schmidt, Nicholas Harvey

The Lottery Ticket Hypothesis for Pre-trained BERT Networks Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Zhangyang Wang, Michael Carbin

Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity Shuxiao Chen, Hangfeng He, Weijie Su

Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples Shafi Goldwasser, Adam Tauman Kalai, Yael Kalai, Omar Montasser

AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows Hadi Mohaghegh Dolatabadi, Sarah Erfani, Christopher Leckie

Few-shot Image Generation with Elastic Weight Consolidation Yijun Li, Richard Zhang, Jingwan (Cynthia) Lu, Eli Shechtman

On the Expressiveness of Approximate Inference in Bayesian Neural Networks Andrew Foong, David Burt, Yingzhen Li, Richard Turner

Non-Crossing Quantile Regression for Distributional Reinforcement Learning Fan Zhou, Jianing Wang, Xingdong Feng

Dark Experience for General Continual Learning: a Strong, Simple Baseline Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, SIMONE CALDERARA

Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping Yujing Hu, Weixun Wang, Hangtian Jia, Yixiang Wang, Yingfeng Chen, Jianye Hao, Feng Wu, Changjie Fan

Neural encoding with visual attention Meenakshi Khosla, Gia Ngo, Keith Jamison, Amy Kuceyeski, Mert Sabuncu

On the linearity of large non-linear models: when and why the tangent kernel is constant Chaoyue Liu, Libin Zhu, Misha Belkin

PLLay: Efficient Topological Layer based on Persistent Landscapes Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Kim, Frederic Chazal, Larry Wasserman

Decentralized Langevin Dynamics for Bayesian Learning Anjaly Parayil, He Bai, Jemin George, Prudhvi Gurram

Shared Space Transfer Learning for analyzing multi-site fMRI data Tony Muhammad Yousefnezhad, Alessandro Selvitella, Daoqiang Zhang, Andrew Greenshaw, Russell Greiner

The Diversified Ensemble Neural Network Shaofeng Zhang, Meng Liu, Junchi Yan

Inductive Quantum Embedding Santosh Kumar Srivastava, Dinesh Khandelwal, Dhiraj Madan, Dinesh Garg, Hima Karanam, L Venkata Subramaniam

Variational Bayesian Unlearning Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet

Batched Coarse Ranking in Multi-Armed Bandits Nikolai Karpov, Qin Zhang

Understanding and Improving Fast Adversarial Training Maksym Andriushchenko, Nicolas Flammarion

Coded Sequential Matrix Multiplication For Straggler Mitigation Nikhil Krishnan Muralee Krishnan, Seyederfan Hosseini, Ashish Khisti

Attack of the Tails: Yes, You Really Can Backdoor Federated Learning Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, Dimitris Papailiopoulos

Certifiably Adversarially Robust Detection of Out-of-Distribution Data Julian Bitterwolf, Alexander Meinke, Matthias Hein

Domain Generalization via Entropy Regularization Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, Dacheng Tao

Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O'Boyle, Amos J. Storkey

Skeleton-bridged Point Completion: From Global Inference to Local Adjustment Yinyu Nie, Yiqun Lin, Xiaoguang Han, Shihui Guo, Jian Chang, Shuguang Cui, Jian.J Zhang

Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato

Improved Guarantees for k-means++ and k-means++ Parallel Konstantin Makarychev, Aravind Reddy, Liren Shan

Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning Anthony Tompkins, Rafael Oliveira, Fabio T. Ramos

An Efficient Adversarial Attack for Tree Ensembles Chong Zhang, Huan Zhang, Cho-Jui Hsieh

Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations Zijie Huang, Yizhou Sun, Wei Wang

Online Bayesian Persuasion Matteo Castiglioni, Andrea Celli, Alberto Marchesi, Nicola Gatti

Robust Pre-Training by Adversarial Contrastive Learning Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang

Random Walk Graph Neural Networks Giannis Nikolentzos, Michalis Vazirgiannis

Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos

Fast and Accurate $k$-means++ via Rejection Sampling Vincent Cohen-Addad, Silvio Lattanzi, Ashkan Norouzi-Fard, Christian Sohler, Ola Svensson

Variational Amodal Object Completion Huan Ling, David Acuna, Karsten Kreis, Seung Wook Kim, Sanja Fidler

When Counterpoint Meets Chinese Folk Melodies Nan Jiang, Sheng Jin, Zhiyao Duan, Changshui Zhang

Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces Hung Tran-The, Sunil Gupta, Santu Rana, Huong Ha, Svetha Venkatesh

Universal Domain Adaptation through Self Supervision Kuniaki Saito, Donghyun Kim, Stan Sclaroff, Kate Saenko

Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning Shreyas Fadnavis, Joshua Batson, Eleftherios Garyfallidis

Stochastic Normalization Zhi Kou, Kaichao You, Mingsheng Long, Jianmin Wang

Constrained episodic reinforcement learning in concave-convex and knapsack settings Kianté Brantley, Miro Dudik, Thodoris Lykouris, Sobhan Miryoosefi, Max Simchowitz, Aleksandrs Slivkins, Wen Sun

On Learning Ising Models under Huber's Contamination Model Adarsh Prasad, Vishwak Srinivasan, Sivaraman Balakrishnan, Pradeep Ravikumar

Cross-validation Confidence Intervals for Test Error Pierre Bayle, Alexandre Bayle, Lucas Janson, Lester Mackey

DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation Alexandre Carlier, Martin Danelljan, Alexandre Alahi, Radu Timofte

Bayesian Attention Modules Xinjie Fan, Shujian Zhang, Bo Chen, Mingyuan Zhou

Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations Kevin Scaman, Cedric Malherbe

SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang, Joun Yeop Lee, Nam Soo Kim

A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network Basile Confavreux, Friedemann Zenke, Everton Agnes, Timothy Lillicrap, Tim Vogels

Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough Mao Ye, Lemeng Wu, Qiang Liu

Path Integral Based Convolution and Pooling for Graph Neural Networks Zheng Ma, Junyu Xuan, Yu Guang Wang, Ming Li, Pietro Liò

Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks Ioana Bica, James Jordon, Mihaela van der Schaar

Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings Heejong Bong, Zongge Liu, Zhao Ren, Matthew Smith, Valerie Ventura, Robert E. Kass

Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds Hassan Hafez-Kolahi, Zeinab Golgooni, Shohreh Kasaei, Mahdieh Soleymani

Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning Weili Nie, Zhiding Yu, Lei Mao, Ankit B. Patel, Yuke Zhu, Anima Anandkumar

GAN Memory with No Forgetting Yulai Cong, Miaoyun Zhao, Jianqiao Li, Sijia Wang, Lawrence Carin

Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Joey Tianyi Zhou, Chengqi Zhang

Gaussian Gated Linear Networks David Budden, Adam Marblestone, Eren Sezener, Tor Lattimore, Gregory Wayne, Joel Veness

Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding Lin Lan, Pinghui Wang, Xuefeng Du, Kaikai Song, Jing Tao, Xiaohong Guan

Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia, Issam Hadj Laradji, Irina Rish, Alexandre Lacoste, David Vázquez, Laurent Charlin

Convex optimization based on global lower second-order models Nikita Doikov, Yurii Nesterov

Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition Tiancheng Jin, Haipeng Luo

Relative gradient optimization of the Jacobian term in unsupervised deep learning Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvarinen

Self-Supervised Visual Representation Learning from Hierarchical Grouping Xiao Zhang, Michael Maire

Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds Valentin Liévin, Andrea Dittadi, Anders Christensen, Ole Winther

Explicit Regularisation in Gaussian Noise Injections Alexander Camuto, Matthew Willetts, Umut Simsekli, Stephen J. Roberts, Chris C. Holmes

Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning Julius Berner, Markus Dablander, Philipp Grohs

Finite-Time Analysis for Double Q-learning Huaqing Xiong, Lin Zhao, Yingbin Liang, Wei Zhang

Learning to Detect Objects with a 1 Megapixel Event Camera Etienne Perot, Pierre de Tournemire, Davide Nitti, Jonathan Masci, Amos Sironi

End-to-End Learning and Intervention in Games Jiayang Li, Jing Yu, Yu Nie, Zhaoran Wang

Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms Dheeraj Nagaraj, Xian Wu, Guy Bresler, Prateek Jain, Praneeth Netrapalli

Predictive coding in balanced neural networks with noise, chaos and delays Jonathan Kadmon, Jonathan Timcheck, Surya Ganguli

Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs Talgat Daulbaev, Alexandr Katrutsa, Larisa Markeeva, Julia Gusak, Andrzej Cichocki, Ivan Oseledets

On the Equivalence between Online and Private Learnability beyond Binary Classification Young Jung, Baekjin Kim, Ambuj Tewari

AViD Dataset: Anonymized Videos from Diverse Countries AJ Piergiovanni, Michael Ryoo

Probably Approximately Correct Constrained Learning Luiz Chamon, Alejandro Ribeiro

RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning Riccardo Del Chiaro, Bartłomiej Twardowski, Andrew Bagdanov, Joost van de Weijer

Decisions, Counterfactual Explanations and Strategic Behavior Stratis Tsirtsis, Manuel Gomez Rodriguez

Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample Shir Gur, Sagie Benaim, Lior Wolf

A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization Digvijay Boob, Qi Deng, Guanghui Lan, Yilin Wang

Reservoir Computing meets Recurrent Kernels and Structured Transforms Jonathan Dong, Ruben Ohana, Mushegh Rafayelyan, Florent Krzakala

Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection Zeyi Huang, Yang Zou, B. V. K. Vijaya Kumar, Dong Huang

Linear Dynamical Systems as a Core Computational Primitive Shiva Kaul

Ratio Trace Formulation of Wasserstein Discriminant Analysis Hexuan Liu, Yunfeng Cai, You-Lin Chen, Ping Li

PAC-Bayes Analysis Beyond the Usual Bounds Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvari, John Shawe-Taylor

Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning Youngsung Kim, Jinwoo Shin, Eunho Yang, Sung Ju Hwang

MPNet: Masked and Permuted Pre-training for Language Understanding Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu

Reinforcement Learning with Feedback Graphs Christoph Dann, Yishay Mansour, Mehryar Mohri, Ayush Sekhari, Karthik Sridharan

Zap Q-Learning With Nonlinear Function Approximation Shuhang Chen, Adithya M Devraj, Fan Lu, Ana Busic, Sean Meyn

Lipschitz-Certifiable Training with a Tight Outer Bound Sungyoon Lee, Jaewook Lee, Saerom Park

Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Rebecca Reiffenhäuser

Conformal Symplectic and Relativistic Optimization Guilherme Franca, Jeremias Sulam, Daniel Robinson, Rene Vidal

Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class Mingyuan Zhang, Shivani Agarwal

Inverting Gradients - How easy is it to break privacy in federated learning? Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, Michael Moeller

Dynamic allocation of limited memory resources in reinforcement learning Nisheet Patel, Luigi Acerbi, Alexandre Pouget

CryptoNAS: Private Inference on a ReLU Budget Zahra Ghodsi, Akshaj Kumar Veldanda, Brandon Reagen, Siddharth Garg

A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm Gersende Fort, Eric Moulines, Hoi-To Wai

CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation Makan Arastuie, Subhadeep Paul, Kevin Xu

SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection Xiaoya Li, Yuxian Meng, Mingxin Zhou, Qinghong Han, Fei Wu, Jiwei Li

Design Space for Graph Neural Networks Jiaxuan You, Zhitao Ying, Jure Leskovec

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis Jungil Kong, Jaehyeon Kim, Jaekyoung Bae

Unbalanced Sobolev Descent Youssef Mroueh, Mattia Rigotti

Identifying Mislabeled Data using the Area Under the Margin Ranking Geoff Pleiss, Tianyi Zhang, Ethan Elenberg, Kilian Q. Weinberger

Combining Deep Reinforcement Learning and Search for Imperfect-Information Games Noam Brown, Anton Bakhtin, Adam Lerer, Qucheng Gong

High-Throughput Synchronous Deep RL Iou-Jen Liu, Raymond Yeh, Alexander Schwing

Contrastive Learning with Adversarial Examples Chih-Hui Ho, Nuno Nvasconcelos

Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables Guangyao Zhou

Adversarial Sparse Transformer for Time Series Forecasting Sifan Wu, Xi Xiao, Qianggang Ding, Peilin Zhao, Ying Wei, Junzhou Huang

The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks Wei Hu, Lechao Xiao, Ben Adlam, Jeffrey Pennington

CLEARER: Multi-Scale Neural Architecture Search for Image Restoration Yuanbiao Gou, Boyun Li, Zitao Liu, Songfan Yang, Xi Peng

Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights Theofanis Karaletsos, Thang D. Bui

Compositional Explanations of Neurons Jesse Mu, Jacob Andreas

Calibrated Reliable Regression using Maximum Mean Discrepancy Peng Cui, Wenbo Hu, Jun Zhu

Directional convergence and alignment in deep learning Ziwei Ji, Matus Telgarsky

Functional Regularization for Representation Learning: A Unified Theoretical Perspective Siddhant Garg, Yingyu Liang

Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits Jack Parker-Holder, Vu Nguyen, Stephen J. Roberts

Understanding Global Feature Contributions With Additive Importance Measures Ian Covert, Scott M. Lundberg, Su-In Lee

Online Non-Convex Optimization with Imperfect Feedback Amélie Héliou, Matthieu Martin, Panayotis Mertikopoulos, Thibaud Rahier

Co-Tuning for Transfer Learning Kaichao You, Zhi Kou, Mingsheng Long, Jianmin Wang

Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu

Continuous Surface Embeddings Natalia Neverova, David Novotny, Marc Szafraniec, Vasil Khalidov, Patrick Labatut, Andrea Vedaldi

Succinct and Robust Multi-Agent Communication With Temporal Message Control Sai Qian Zhang, Qi Zhang, Jieyu Lin

Big Bird: Transformers for Longer Sequences Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed

Neural Execution Engines: Learning to Execute Subroutines Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

Random Reshuffling: Simple Analysis with Vast Improvements Konstantin Mishchenko, Ahmed Khaled, Peter Richtarik

Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors Karl Pertsch, Oleh Rybkin, Frederik Ebert, Shenghao Zhou, Dinesh Jayaraman, Chelsea Finn, Sergey Levine

Statistical Optimal Transport posed as Learning Kernel Embedding Saketha Nath Jagarlapudi, Pratik Kumar Jawanpuria

Dual-Resolution Correspondence Networks Xinghui Li, Kai Han, Shuda Li, Victor Prisacariu

Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization Abhinav Agrawal, Daniel R. Sheldon, Justin Domke

f-Divergence Variational Inference Neng Wan, Dapeng Li, NAIRA HOVAKIMYAN

Unfolding recurrence by Green’s functions for optimized reservoir computing Sandra Nestler, Christian Keup, David Dahmen, Matthieu Gilson, Holger Rauhut, Moritz Helias

The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification Yihao Lv, Youzhi Gu, Liu Xinggao

Disentangling by Subspace Diffusion David Pfau, Irina Higgins, Alex Botev, Sébastien Racanière

Towards Neural Programming Interfaces Zachary Brown, Nathaniel Robinson, David Wingate, Nancy Fulda

Discovering Symbolic Models from Deep Learning with Inductive Biases Miles Cranmer, Alvaro Sanchez Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho

Real World Games Look Like Spinning Tops Wojciech M. Czarnecki, Gauthier Gidel, Brendan Tracey, Karl Tuyls, Shayegan Omidshafiei, David Balduzzi, Max Jaderberg

Cooperative Heterogeneous Deep Reinforcement Learning Han Zheng, Pengfei Wei, Jing Jiang, Guodong Long, Qinghua Lu, Chengqi Zhang

Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization Hung-Jen Chen, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun

ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool Gellert Weisz, András György, Wei-I Lin, Devon Graham, Kevin Leyton-Brown, Csaba Szepesvari, Brendan Lucier

Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs Xiangru Huang, Haitao Yang, Etienne Vouga, Qixing Huang

Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms. Sriram Sankaranarayanan, Yi Chou, Eric Goubault, Sylvie Putot

Applications of Common Entropy for Causal Inference Murat Kocaoglu, Sanjay Shakkottai, Alexandros G. Dimakis, Constantine Caramanis, Sriram Vishwanath

SGD with shuffling: optimal rates without component convexity and large epoch requirements Kwangjun Ahn, Chulhee Yun, Suvrit Sra

Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models Leonardo Cotta, Carlos H. C. Teixeira, Ananthram Swami, Bruno Ribeiro

Neural Manifold Ordinary Differential Equations Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser Nam Lim, Christopher M. De Sa

CO-Optimal Transport Vayer Titouan, Ievgen Redko, Rémi Flamary, Nicolas Courty

Continuous Meta-Learning without Tasks James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone

A mathematical theory of cooperative communication Pei Wang, Junqi Wang, Pushpi Paranamana, Patrick Shafto

Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets Avetik Karagulyan, Arnak Dalalyan

Learning Invariances in Neural Networks from Training Data Gregory Benton, Marc Finzi, Pavel Izmailov, Andrew G. Wilson

A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods Yue Frank Wu, Weitong ZHANG, Pan Xu, Quanquan Gu

Pruning Filter in Filter Fanxu Meng, Hao Cheng, Ke Li, Huixiang Luo, Xiaowei Guo, Guangming Lu, Xing Sun

Learning to Mutate with Hypergradient Guided Population Zhiqiang Tao, Yaliang Li, Bolin Ding, Ce Zhang, Jingren Zhou, Yun Fu

A convex optimization formulation for multivariate regression Yunzhang Zhu

Online Meta-Critic Learning for Off-Policy Actor-Critic Methods Wei Zhou, Yiying Li, Yongxin Yang, Huaimin Wang, Timothy Hospedales

The All-or-Nothing Phenomenon in Sparse Tensor PCA Jonathan Niles-Weed, Ilias Zadik

Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis Kavi Gupta, Peter Ebert Christensen, Xinyun Chen, Dawn Song

ARMA Nets: Expanding Receptive Field for Dense Prediction Jiahao Su, Shiqi Wang, Furong Huang

Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations Mina Konakovic Lukovic, Yunsheng Tian, Wojciech Matusik

SOLOv2: Dynamic and Fast Instance Segmentation Xinlong Wang, Rufeng Zhang, Tao Kong, Lei Li, Chunhua Shen

Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization Chong You, Zhihui Zhu, Qing Qu, Yi Ma

Axioms for Learning from Pairwise Comparisons Ritesh Noothigattu, Dominik Peters, Ariel D. Procaccia

Continuous Regularized Wasserstein Barycenters Lingxiao Li, Aude Genevay, Mikhail Yurochkin, Justin M. Solomon

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang

Online Multitask Learning with Long-Term Memory Mark Herbster, Stephen Pasteris, Lisa Tse

Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies Yuehua Zhu, Muli Yang, Cheng Deng, Wei Liu

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting LEI BAI, Lina Yao, Can Li, Xianzhi Wang, Can Wang

On Reward-Free Reinforcement Learning with Linear Function Approximation Ruosong Wang, Simon S. Du, Lin Yang, Russ R. Salakhutdinov

Robustness of Community Detection to Random Geometric Perturbations Sandrine Peche, Vianney Perchet

Learning outside the Black-Box: The pursuit of interpretable models Jonathan Crabbe, Yao Zhang, William Zame, Mihaela van der Schaar

Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization Xuefeng GAO, Mert Gurbuzbalaban, Lingjiong Zhu

Robust large-margin learning in hyperbolic space Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya K. Menon, Sanjiv Kumar

Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets Rui Luo, Qiang Zhang, Yaodong Yang, Jun Wang

Adversarially Robust Few-Shot Learning: A Meta-Learning Approach Micah Goldblum, Liam Fowl, Tom Goldstein

Neural Anisotropy Directions Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi, Pascal Frossard

Digraph Inception Convolutional Networks Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum, Andrew Lim

PAC-Bayesian Bound for the Conditional Value at Risk Zakaria Mhammedi, Benjamin Guedj, Robert C. Williamson

Stochastic Stein Discrepancies Jackson Gorham, Anant Raj, Lester Mackey

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs Ignavier Ng, AmirEmad Ghassami, Kun Zhang

Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search Houwen Peng, Hao Du, Hongyuan Yu, QI LI, Jing Liao, Jianlong Fu

Fair Multiple Decision Making Through Soft Interventions Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu

Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment Yuan Chen, Donglin Zeng, Tianchen Xu, Yuanjia Wang

Learning to Play No-Press Diplomacy with Best Response Policy Iteration Thomas Anthony, Tom Eccles, Andrea Tacchetti, János Kramár, Ian Gemp, Thomas Hudson, Nicolas Porcel, Marc Lanctot, Julien Perolat, Richard Everett, Satinder Singh, Thore Graepel, Yoram Bachrach

Inverse Learning of Symmetries Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth

DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling Moshe Eliasof, Eran Treister

Distributed Newton Can Communicate Less and Resist Byzantine Workers Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar

Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees Shali Jiang, Daniel Jiang, Maximilian Balandat, Brian Karrer, Jacob Gardner, Roman Garnett

Effective Diversity in Population Based Reinforcement Learning Jack Parker-Holder, Aldo Pacchiano, Krzysztof M. Choromanski, Stephen J. Roberts

Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data Utkarsh Ojha, Krishna Kumar Singh, Cho-Jui Hsieh, Yong Jae Lee

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow

Hybrid Models for Learning to Branch Prateek Gupta, Maxime Gasse, Elias Khalil, Pawan Mudigonda, Andrea Lodi, Yoshua Bengio

WoodFisher: Efficient Second-Order Approximation for Neural Network Compression Sidak Pal Singh, Dan Alistarh

Bi-level Score Matching for Learning Energy-based Latent Variable Models Fan Bao, Chongxuan LI, Kun Xu, Hang Su, Jun Zhu, Bo Zhang

Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding Zhu Zhang, Zhou Zhao, Zhijie Lin, jieming zhu, Xiuqiang He

Decision trees as partitioning machines to characterize their generalization properties Jean-Samuel Leboeuf, Frédéric LeBlanc, Mario Marchand

Learning to Prove Theorems by Learning to Generate Theorems Mingzhe Wang, Jia Deng

3D Self-Supervised Methods for Medical Imaging Aiham Taleb, Winfried Loetzsch, Noel Danz, Julius Severin, Thomas Gaertner, Benjamin Bergner, Christoph Lippert

Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods Laurence Aitchison

Worst-Case Analysis for Randomly Collected Data Justin Chen, Gregory Valiant, Paul Valiant

Truthful Data Acquisition via Peer Prediction Yiling Chen, Yiheng Shen, Shuran Zheng

Learning Robust Decision Policies from Observational Data Muhammad Osama, Dave Zachariah, Peter Stoica

Byzantine Resilient Distributed Multi-Task Learning Jiani Li, Waseem Abbas, Xenofon Koutsoukos

Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting Ziping Xu, Ambuj Tewari

Improving model calibration with accuracy versus uncertainty optimization Ranganath Krishnan, Omesh Tickoo

The Convolution Exponential and Generalized Sylvester Flows Emiel Hoogeboom, Victor Garcia Satorras, Jakub Tomczak, Max Welling

An Improved Analysis of Stochastic Gradient Descent with Momentum Yanli Liu, Yuan Gao, Wotao Yin

Precise expressions for random projections: Low-rank approximation and randomized Newton Michal Derezinski, Feynman T. Liang, Zhenyu Liao, Michael W. Mahoney

The MAGICAL Benchmark for Robust Imitation Sam Toyer, Rohin Shah, Andrew Critch, Stuart Russell

X-CAL: Explicit Calibration for Survival Analysis Mark Goldstein, Xintian Han, Aahlad Puli, Adler Perotte, Rajesh Ranganath

Decentralized Accelerated Proximal Gradient Descent Haishan Ye, Ziang Zhou, Luo Luo, Tong Zhang

Making Non-Stochastic Control (Almost) as Easy as Stochastic Max Simchowitz

BERT Loses Patience: Fast and Robust Inference with Early Exit Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian McAuley, Ke Xu, Furu Wei

Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization Dmitry Kovalev, Adil Salim, Peter Richtarik

BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning Xinyue Chen, Zijian Zhou, Zheng Wang, Che Wang, Yanqiu Wu, Keith Ross

Regularizing Towards Permutation Invariance In Recurrent Models Edo Cohen-Karlik, Avichai Ben David, Amir Globerson

What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes Herman Yau, Chris Russell, Simon Hadfield

Batch normalization provably avoids ranks collapse for randomly initialised deep networks Hadi Daneshmand, Jonas Kohler, Francis Bach, Thomas Hofmann, Aurelien Lucchi

Choice Bandits Arpit Agarwal, Nicholas Johnson, Shivani Agarwal

What if Neural Networks had SVDs? Alexander Mathiasen, Frederik Hvilshøj, Jakob Rødsgaard Jørgensen, Anshul Nasery, Davide Mottin

A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices Jiezhong Qiu, Chi Wang, Ben Liao, Richard Peng, Jie Tang

CoMIR: Contrastive Multimodal Image Representation for Registration Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, Natasa Sladoje

Ensuring Fairness Beyond the Training Data Debmalya Mandal, Samuel Deng, Suman Jana, Jeannette Wing, Daniel J. Hsu

How do fair decisions fare in long-term qualification? Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellstrom, Kun Zhang, Cheng Zhang

Pre-training via Paraphrasing Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan, Sida Wang, Luke Zettlemoyer

GCN meets GPU: Decoupling “When to Sample” from “How to Sample” Morteza Ramezani, Weilin Cong, Mehrdad Mahdavi, Anand Sivasubramaniam, Mahmut Kandemir

Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks Zixuan Ke, Bing Liu, Xingchang Huang

All your loss are belong to Bayes Christian Walder, Richard Nock

HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W. Mahoney, Kurt Keutzer

Sample-Efficient Reinforcement Learning of Undercomplete POMDPs Chi Jin, Sham Kakade, Akshay Krishnamurthy, Qinghua Liu

Non-Convex SGD Learns Halfspaces with Adversarial Label Noise Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis

A Tight Lower Bound and Efficient Reduction for Swap Regret Shinji Ito

DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction Aviral Kumar, Abhishek Gupta, Sergey Levine

OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling Viet Huynh, He Zhao, Dinh Phung

Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, Ludwig Schmidt

Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference Disi Ji, Padhraic Smyth, Mark Steyvers

RandAugment: Practical Automated Data Augmentation with a Reduced Search Space Ekin Dogus Cubuk, Barret Zoph, Jon Shlens, Quoc Le

Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model Yiwei Shen, Pierre C Bellec

DisARM: An Antithetic Gradient Estimator for Binary Latent Variables Zhe Dong, Andriy Mnih, George Tucker

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings Pantelis Elinas, Edwin V. Bonilla, Louis Tiao

Supervised Contrastive Learning Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, Dilip Krishnan

Learning Optimal Representations with the Decodable Information Bottleneck Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam

Meta-trained agents implement Bayes-optimal agents Vladimir Mikulik, Grégoire Delétang, Tom McGrath, Tim Genewein, Miljan Martic, Shane Legg, Pedro Ortega

Learning Agent Representations for Ice Hockey Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan

Weak Form Generalized Hamiltonian Learning Kevin Course, Trefor Evans, Prasanth Nair

Neural Non-Rigid Tracking Aljaz Bozic, Pablo Palafox, Michael Zollhöfer, Angela Dai, Justus Thies, Matthias Niessner

Collegial Ensembles Etai Littwin, Ben Myara, Sima Sabah, Joshua Susskind, Shuangfei Zhai, Oren Golan

ICNet: Intra-saliency Correlation Network for Co-Saliency Detection Wen-Da Jin, Jun Xu, Ming-Ming Cheng, Yi Zhang, Wei Guo

Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows Cheng Zhang

Deep Metric Learning with Spherical Embedding Dingyi Zhang, Yingming Li, Zhongfei Zhang

Preference-based Reinforcement Learning with Finite-Time Guarantees Yichong Xu, Ruosong Wang, Lin Yang, Aarti Singh, Artur Dubrawski

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar C. Tatikonda, Nicha Dvornek, Xenophon Papademetris, James Duncan

Interpretable Sequence Learning for Covid-19 Forecasting Sercan Arik, Chun-Liang Li, Jinsung Yoon, Rajarishi Sinha, Arkady Epshteyn, Long Le, Vikas Menon, Shashank Singh, Leyou Zhang, Martin Nikoltchev, Yash Sonthalia, Hootan Nakhost, Elli Kanal, Tomas Pfister

Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, Emma Brunskill

Modern Hopfield Networks and Attention for Immune Repertoire Classification Michael Widrich, Bernhard Schäfl, Milena Pavlović, Hubert Ramsauer, Lukas Gruber, Markus Holzleitner, Johannes Brandstetter, Geir Kjetil Sandve, Victor Greiff, Sepp Hochreiter, Günter Klambauer

One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers Heng Yang, Luca Carlone

Task-Robust Model-Agnostic Meta-Learning Liam Collins, Aryan Mokhtari, Sanjay Shakkottai

R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making Sergey Shuvaev, Sarah Starosta, Duda Kvitsiani, Adam Kepecs, Alexei Koulakov

Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity Dan Garber

Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev Xiao Wang, Qi Lei, Ioannis Panageas

Tensor Completion Made Practical Allen Liu, Ankur Moitra

Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks Kenta Oono, Taiji Suzuki

Content Provider Dynamics and Coordination in Recommendation Ecosystems Omer Ben-Porat, Itay Rosenberg, Moshe Tennenholtz

Almost Surely Stable Deep Dynamics Nathan Lawrence, Philip Loewen, Michael Forbes, Johan Backstrom, Bhushan Gopaluni

Experimental design for MRI by greedy policy search Tim Bakker, Herke van Hoof, Max Welling

Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation Aaron Sonabend, Junwei Lu, Leo Anthony Celi, Tianxi Cai, Peter Szolovits

ColdGANs: Taming Language GANs with Cautious Sampling Strategies Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano

Hedging in games: Faster convergence of external and swap regrets Xi Chen, Binghui Peng

The Origins and Prevalence of Texture Bias in Convolutional Neural Networks Katherine Hermann, Ting Chen, Simon Kornblith

Time-Reversal Symmetric ODE Network In Huh, Eunho Yang, Sung Ju Hwang, Jinwoo Shin

Provable Overlapping Community Detection in Weighted Graphs Jimit Majmudar, Stephen Vavasis

Fast Unbalanced Optimal Transport on a Tree Ryoma Sato, Makoto Yamada, Hisashi Kashima

Acceleration with a Ball Optimization Oracle Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, Aaron Sidford, Kevin Tian

Avoiding Side Effects By Considering Future Tasks Victoria Krakovna, Laurent Orseau, Richard Ngo, Miljan Martic, Shane Legg

Handling Missing Data with Graph Representation Learning Jiaxuan You, Xiaobai Ma, Yi Ding, Mykel J. Kochenderfer, Jure Leskovec

Improving Auto-Augment via Augmentation-Wise Weight Sharing Keyu Tian, Chen Lin, Ming Sun, Luping Zhou, Junjie Yan, Wanli Ouyang

MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles Zhennan Wang, Canqun Xiang, Wenbin Zou, Chen Xu

HRN: A Holistic Approach to One Class Learning Wenpeng Hu, Mengyu Wang, Qi Qin, Jinwen Ma, Bing Liu

The Generalized Lasso with Nonlinear Observations and Generative Priors Zhaoqiang Liu, Jonathan Scarlett

Fair regression via plug-in estimator and recalibration with statistical guarantees Evgenii Chzhen, Christophe Denis, Mohamed Hebiri, Luca Oneto, Massimiliano Pontil

Modeling Shared responses in Neuroimaging Studies through MultiView ICA Hugo Richard, Luigi Gresele, Aapo Hyvarinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin

Efficient Planning in Large MDPs with Weak Linear Function Approximation Roshan Shariff, Csaba Szepesvari

Efficient Learning of Generative Models via Finite-Difference Score Matching Tianyu Pang, Kun Xu, Chongxuan LI, Yang Song, Stefano Ermon, Jun Zhu

Semialgebraic Optimization for Lipschitz Constants of ReLU Networks Tong Chen, Jean B. Lasserre, Victor Magron, Edouard Pauwels

Linear-Sample Learning of Low-Rank Distributions Ayush Jain, Alon Orlitsky

Transferable Calibration with Lower Bias and Variance in Domain Adaptation Ximei Wang, Mingsheng Long, Jianmin Wang, Michael Jordan

Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics Taiji Suzuki

Online Bayesian Goal Inference for Boundedly Rational Planning Agents Tan Zhi-Xuan, Jordyn Mann, Tom Silver, Josh Tenenbaum, Vikash Mansinghka

BayReL: Bayesian Relational Learning for Multi-omics Data Integration Ehsan Hajiramezanali, Arman Hasanzadeh, Nick Duffield, Krishna Narayanan, Xiaoning Qian

Weakly Supervised Deep Functional Maps for Shape Matching Abhishek Sharma, Maks Ovsjanikov

Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, Geoffrey J. Gordon

Rethinking the Value of Labels for Improving Class-Imbalanced Learning Yuzhe Yang, Zhi Xu

Provably Robust Metric Learning Lu Wang, Xuanqing Liu, Jinfeng Yi, Yuan Jiang, Cho-Jui Hsieh

Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings Yu Chen, Lingfei Wu, Mohammed Zaki

COPT: Coordinated Optimal Transport on Graphs Yihe Dong, Will Sawin

No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems Nimit Sohoni, Jared Dunnmon, Geoffrey Angus, Albert Gu, Christopher Ré

Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets Kai Han, Yunhe Wang, Qiulin Zhang, Wei Zhang, Chunjing XU, Tong Zhang

Self-Adaptive Training: beyond Empirical Risk Minimization Lang Huang, Chao Zhang, Hongyang Zhang

Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization Jonathan Lacotte, Mert Pilanci

Near-Optimal Comparison Based Clustering Michaël Perrot, Pascal Esser, Debarghya Ghoshdastidar

Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement Xin Liu, Josh Fromm, Shwetak Patel, Daniel McDuff

A new convergent variant of Q-learning with linear function approximation Diogo Carvalho, Francisco S. Melo, Pedro Santos

TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation Chun-Hsing Lin, Siang-Ruei Wu, Hung-yi Lee, Yun-Nung Chen

Neural Networks with Small Weights and Depth-Separation Barriers Gal Vardi, Ohad Shamir

Untangling tradeoffs between recurrence and self-attention in artificial neural networks Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal ALIAS PARTH GOYAL, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie

Dual-Free Stochastic Decentralized Optimization with Variance Reduction Hadrien Hendrikx, Francis Bach, Laurent Massoulié

Online Learning in Contextual Bandits using Gated Linear Networks Eren Sezener, Marcus Hutter, David Budden, Jianan Wang, Joel Veness

Throughput-Optimal Topology Design for Cross-Silo Federated Learning Othmane MARFOQ, CHUAN XU, Giovanni Neglia, Richard Vidal

Quantized Variational Inference Amir Dib

Asymptotically Optimal Exact Minibatch Metropolis-Hastings Ruqi Zhang, A. Feder Cooper, Christopher M. De Sa

Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search Linnan Wang, Rodrigo Fonseca, Yuandong Tian

Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests Sean Kulinski, Saurabh Bagchi, David I. Inouye

Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks Jinseok Kim, Kyungsu Kim, Jae-Joon Kim

Space-Time Correspondence as a Contrastive Random Walk Allan Jabri, Andrew Owens, Alexei Efros

The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space Adam Smith, Shuang Song, Abhradeep Guha Thakurta

Exponential ergodicity of mirror-Langevin diffusions Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, Austin Stromme

An Efficient Framework for Clustered Federated Learning Avishek Ghosh, Jichan Chung, Dong Yin, Kannan Ramchandran

Autoencoders that don't overfit towards the Identity Harald Steck

Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond Gabriele Farina, Tuomas Sandholm

Parameterized Explainer for Graph Neural Network Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang

Recursive Inference for Variational Autoencoders Minyoung Kim, Vladimir Pavlovic

Flexible mean field variational inference using mixtures of non-overlapping exponential families Jeffrey Spence

HYDRA: Pruning Adversarially Robust Neural Networks Vikash Sehwag, Shiqi Wang, Prateek Mittal, Suman Jana

NVAE: A Deep Hierarchical Variational Autoencoder Arash Vahdat, Jan Kautz

Can Temporal-Diﬀerence and Q-Learning Learn Representation? A Mean-Field Theory Yufeng Zhang, Qi Cai, Zhuoran Yang, Yongxin Chen, Zhaoran Wang

What Do Neural Networks Learn When Trained With Random Labels? Hartmut Maennel, Ibrahim M. Alabdulmohsin, Ilya O. Tolstikhin, Robert Baldock, Olivier Bousquet, Sylvain Gelly, Daniel Keysers

Counterfactual Prediction for Bundle Treatment Hao Zou, Peng Cui, Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, Yue He

Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs Hongyu Ren, Jure Leskovec

Learning Disentangled Representations and Group Structure of Dynamical Environments Robin Quessard, Thomas Barrett, William Clements

Learning Linear Programs from Optimal Decisions Yingcong Tan, Daria Terekhov, Andrew Delong

Wisdom of the Ensemble: Improving Consistency of Deep Learning Models Lijing Wang, Dipanjan Ghosh, Maria Gonzalez Diaz, Ahmed Farahat, Mahbubul Alam, Chetan Gupta, Jiangzhuo Chen, Madhav Marathe

Universal Function Approximation on Graphs Rickard Brüel Gabrielsson

Accelerating Reinforcement Learning through GPU Atari Emulation Steven Dalton, iuri frosio

EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning Jiachen Li, Fan Yang, Masayoshi Tomizuka, Chiho Choi

Comparator-Adaptive Convex Bandits Dirk van der Hoeven, Ashok Cutkosky, Haipeng Luo

Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs Jianzhun Du, Joseph Futoma, Finale Doshi-Velez

The Adaptive Complexity of Maximizing a Gross Substitutes Valuation Ron Kupfer, Sharon Qian, Eric Balkanski, Yaron Singer

A Robust Functional EM Algorithm for Incomplete Panel Count Data Alexander Moreno, Zhenke Wu, Jamie Roslyn Yap, Cho Lam, David Wetter, Inbal Nahum-Shani, Walter Dempsey, James M. Rehg

Graph Stochastic Neural Networks for Semi-supervised Learning Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, Jilong Wang

Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition Dat Huynh, Ehsan Elhamifar

A Benchmark for Systematic Generalization in Grounded Language Understanding Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, Brenden M. Lake

Weston-Watkins Hinge Loss and Ordered Partitions Yutong Wang, Clayton Scott

Reinforcement Learning with Augmented Data Misha Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas

Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes Yi Tian, Jian Qian, Suvrit Sra

Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning Runzhong Wang, Junchi Yan, Xiaokang Yang

Estimating Training Data Influence by Tracing Gradient Descent Garima Pruthi, Frederick Liu, Satyen Kale, Mukund Sundararajan

Joint Policy Search for Multi-agent Collaboration with Imperfect Information Yuandong Tian, Qucheng Gong, Yu Jiang

Adversarial Bandits with Corruptions: Regret Lower Bound and No-regret Algorithm lin yang, Mohammad Hajiesmaili, Mohammad Sadegh Talebi, John C. S. Lui, Wing Shing Wong

Beta R-CNN: Looking into Pedestrian Detection from Another Perspective Zixuan Xu, Banghuai Li, Ye Yuan, Anhong Dang

Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks Soham De, Sam Smith

Learning Retrospective Knowledge with Reverse Reinforcement Learning Shangtong Zhang, Vivek Veeriah, Shimon Whiteson

Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data Michael Cogswell, Jiasen Lu, Rishabh Jain, Stefan Lee, Devi Parikh, Dhruv Batra

GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs Sahil Manchanda, AKASH MITTAL, Anuj Dhawan, Sourav Medya, Sayan Ranu, Ambuj Singh

A General Large Neighborhood Search Framework for Solving Integer Linear Programs Jialin Song, ravi lanka, Yisong Yue, Bistra Dilkina

A Theoretical Framework for Target Propagation Alexander Meulemans, Francesco Carzaniga, Johan Suykens, João Sacramento, Benjamin F. Grewe

OrganITE: Optimal transplant donor organ offering using an individual treatment effect Jeroen Berrevoets, James Jordon, Ioana Bica, alexander gimson, Mihaela van der Schaar

The Complete Lasso Tradeoff Diagram Hua Wang, Yachong Yang, Zhiqi Bu, Weijie Su

On the universality of deep learning Emmanuel Abbe, Colin Sandon

Regression with reject option and application to kNN Ahmed Zaoui, Christophe Denis, Mohamed Hebiri

The Primal-Dual method for Learning Augmented Algorithms Etienne Bamas, Andreas Maggiori, Ola Svensson

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, Wen Sun

A Class of Algorithms for General Instrumental Variable Models Niki Kilbertus, Matt J. Kusner, Ricardo Silva

Black-Box Ripper: Copying black-box models using generative evolutionary algorithms Antonio Barbalau, Adrian Cosma, Radu Tudor Ionescu, Marius Popescu

Bayesian Optimization of Risk Measures Sait Cakmak, Raul Astudillo Marban, Peter Frazier, Enlu Zhou

TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search Tarun Gogineni, Ziping Xu, Exequiel Punzalan, Runxuan Jiang, Joshua Kammeraad, Ambuj Tewari, Paul Zimmerman

GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis Katja Schwarz, Yiyi Liao, Michael Niemeyer, Andreas Geiger

PIE-NET: Parametric Inference of Point Cloud Edges Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali Mahdavi-Amiri, Hao Zhang

A Simple Language Model for Task-Oriented Dialogue Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, Semih Yavuz, Richard Socher

A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval Fan Wu, Patrick Rebeschini

Confidence sequences for sampling without replacement Ian Waudby-Smith, Aaditya Ramdas

A mean-field analysis of two-player zero-sum games Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant Rotskoff, Joan Bruna

Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge Alon Talmor, Oyvind Tafjord, Peter Clark, Yoav Goldberg, Jonathan Berant

Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games Stephen Mcaleer, JB Lanier, Roy Fox, Pierre Baldi

Improving Sparse Vector Technique with Renyi Differential Privacy Yuqing Zhu, Yu-Xiang Wang

Latent Template Induction with Gumbel-CRFs Yao Fu, Chuanqi Tan, Bin Bi, Mosha Chen, Yansong Feng, Alexander Rush

Instance Based Approximations to Profile Maximum Likelihood Nima Anari, Moses Charikar, Kirankumar Shiragur, Aaron Sidford

Factorizable Graph Convolutional Networks Yiding Yang, Zunlei Feng, Mingli Song, Xinchao Wang

Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses Gaurang Sriramanan, Sravanti Addepalli, Arya Baburaj, Venkatesh Babu R

A Study on Encodings for Neural Architecture Search Colin White, Willie Neiswanger, Sam Nolen, Yash Savani

Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising Yaochen Xie, Zhengyang Wang, Shuiwang Ji

Early-Learning Regularization Prevents Memorization of Noisy Labels Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda

LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond Wenbo Li, Kun Zhou, Lu Qi, Nianjuan Jiang, Jiangbo Lu, Jiaya Jia

Learning Parities with Neural Networks Amit Daniely, Eran Malach

Consistent Plug-in Classifiers for Complex Objectives and Constraints Shiv Kumar Tavker, Harish Guruprasad Ramaswamy, Harikrishna Narasimhan

Movement Pruning: Adaptive Sparsity by Fine-Tuning Victor Sanh, Thomas Wolf, Alexander Rush

Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot Jingtong Su, Yihang Chen, Tianle Cai, Tianhao Wu, Ruiqi Gao, Liwei Wang, Jason D. Lee

Online Matrix Completion with Side Information Mark Herbster, Stephen Pasteris, Lisa Tse

Position-based Scaled Gradient for Model Quantization and Pruning Jangho Kim, KiYoon Yoo, Nojun Kwak

Online Learning with Primary and Secondary Losses Avrim Blum, Han Shao

Graph Information Bottleneck Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec

The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise Ilias Diakonikolas, Daniel M. Kane, Pasin Manurangsi

Adaptive Online Estimation of Piecewise Polynomial Trends Dheeraj Baby, Yu-Xiang Wang

RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, Prateek Jain

Agnostic Learning with Multiple Objectives Corinna Cortes, Mehryar Mohri, Javier Gonzalvo, Dmitry Storcheus

3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data Benjamin Biggs, David Novotny, Sebastien Ehrhardt, Hanbyul Joo, Ben Graham, Andrea Vedaldi

Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation Yangxin Wu, Gengwei Zhang, Hang Xu, Xiaodan Liang, Liang Lin

Differentiable Top-k with Optimal Transport Yujia Xie, Hanjun Dai, Minshuo Chen, Bo Dai, Tuo Zhao, Hongyuan Zha, Wei Wei, Tomas Pfister

Information-theoretic Task Selection for Meta-Reinforcement Learning Ricardo Luna Gutierrez, Matteo Leonetti

A Limitation of the PAC-Bayes Framework Roi Livni, Shay Moran

On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Tomas Pfister, Pradeep Ravikumar

Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems Luo Luo, Haishan Ye, Zhichao Huang, Tong Zhang

Why Normalizing Flows Fail to Detect Out-of-Distribution Data Polina Kirichenko, Pavel Izmailov, Andrew G. Wilson

Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay Joao Marques-Silva, Thomas Gerspacher, Martin Cooper, Alexey Ignatiev, Nina Narodytska

Unsupervised Translation of Programming Languages Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, Guillaume Lample

Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation Yawei Luo, Ping Liu, Tao Guan, Junqing Yu, Yi Yang

Optimally Deceiving a Learning Leader in Stackelberg Games Georgios Birmpas, Jiarui Gan, Alexandros Hollender, Francisco Marmolejo, Ninad Rajgopal, Alexandros Voudouris

Online Optimization with Memory and Competitive Control Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman

IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method Yossi Arjevani, Joan Bruna, Bugra Can, Mert Gurbuzbalaban, Stefanie Jegelka, Hongzhou Lin

Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation Zhiwei Deng, Karthik Narasimhan, Olga Russakovsky

Learning from Failure: De-biasing Classifier from Biased Classifier Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, Jinwoo Shin

Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder Zhisheng Xiao, Qing Yan, Yali Amit

Deep Diffusion-Invariant Wasserstein Distributional Classification Sung Woo Park, Dong Wook Shu, Junseok Kwon

Finding All $\epsilon$-Good Arms in Stochastic Bandits Blake Mason, Lalit Jain, Ardhendu Tripathy, Robert Nowak

Meta-Learning through Hebbian Plasticity in Random Networks Elias Najarro, Sebastian Risi

A Computational Separation between Private Learning and Online Learning Mark Bun

Top-KAST: Top-K Always Sparse Training Siddhant Jayakumar, Razvan Pascanu, Jack Rae, Simon Osindero, Erich Elsen

Meta-Learning with Adaptive Hyperparameters Sungyong Baik, Myungsub Choi, Janghoon Choi, Heewon Kim, Kyoung Mu Lee

Tight last-iterate convergence rates for no-regret learning in multi-player games Noah Golowich, Sarath Pattathil, Constantinos Daskalakis

Curvature Regularization to Prevent Distortion in Graph Embedding Hongbin Pei, Bingzhe Wei, Kevin Chang, Chunxu Zhang, Bo Yang

Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability Nathan Inkawhich, Kevin Liang, Binghui Wang, Matthew Inkawhich, Lawrence Carin, Yiran Chen

Statistical and Topological Properties of Sliced Probability Divergences Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli

Probabilistic Active Meta-Learning Jean Kaddour, Steindor Saemundsson, Marc Deisenroth (he/him)

Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher Guangda Ji, Zhanxing Zhu

Adversarial Attacks on Deep Graph Matching Zijie Zhang, Zeru Zhang, Yang Zhou, Yelong Shen, Ruoming Jin, Dejing Dou

The Generalization-Stability Tradeoff In Neural Network Pruning Brian Bartoldson, Ari Morcos, Adrian Barbu, Gordon Erlebacher

Gradient-EM Bayesian Meta-Learning Yayi Zou, Xiaoqi Lu

Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

Linearly Converging Error Compensated SGD Eduard Gorbunov, Dmitry Kovalev, Dmitry Makarenko, Peter Richtarik

Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction David Novotny, Roman Shapovalov, Andrea Vedaldi

A Self-Tuning Actor-Critic Algorithm Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado P. van Hasselt, David Silver, Satinder Singh

The Cone of Silence: Speech Separation by Localization Teerapat Jenrungrot, Vivek Jayaram, Steve Seitz, Ira Kemelmacher-Shlizerman

High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds Noémie Jaquier, Leonel Rozo

Train-by-Reconnect: Decoupling Locations of Weights from Their Values Yushi Qiu, Reiji Suda

Learning discrete distributions: user vs item-level privacy Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X. Yu, Sanjiv Kumar, Michael Riley

Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula Yuxuan Zhao, Madeleine Udell

Sparse and Continuous Attention Mechanisms André Martins, António Farinhas, Marcos Treviso, Vlad Niculae, Pedro Aguiar, Mario Figueiredo

Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, Jian Yang

Learning by Minimizing the Sum of Ranked Range Shu Hu, Yiming Ying, xin wang, Siwei Lyu

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations Huan Zhang, Hongge Chen, Chaowei Xiao, Bo Li, Mingyan Liu, Duane Boning, Cho-Jui Hsieh

Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features Robin Schirrmeister, Yuxuan Zhou, Tonio Ball, Dan Zhang

Fair Hierarchical Clustering Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang

Self-training Avoids Using Spurious Features Under Domain Shift Yining Chen, Colin Wei, Ananya Kumar, Tengyu Ma

Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions Shom Banerjee

CircleGAN: Generative Adversarial Learning across Spherical Circles Woohyeon Shim, Minsu Cho

WOR and $p$'s: Sketches for $\ell_p$-Sampling Without Replacement Edith Cohen, Rasmus Pagh, David Woodruff

Hypersolvers: Toward Fast Continuous-Depth Models Michael Poli, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, Jinkyoo Park

Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment Ben Usman, Avneesh Sud, Nick Dufour, Kate Saenko

Escaping the Gravitational Pull of Softmax Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvari, Dale Schuurmans

Regret in Online Recommendation Systems Kaito Ariu, Narae Ryu, Se-Young Yun, Alexandre Proutiere

On Convergence and Generalization of Dropout Training Poorya Mianjy, Raman Arora

Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari

Implicit Regularization in Deep Learning May Not Be Explainable by Norms Noam Razin, Nadav Cohen

POMO: Policy Optimization with Multiple Optima for Reinforcement Learning Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon, Youngjune Gwon, Seungjai Min

Uncertainty-aware Self-training for Few-shot Text Classification Subhabrata Mukherjee, Ahmed Awadallah

Learning to Learn with Feedback and Local Plasticity Jack Lindsey, Ashok Litwin-Kumar

Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization Qi Chen, Lin Sun, Ernest Cheung, Alan L. Yuille

Sharper Generalization Bounds for Pairwise Learning Yunwen Lei, Antoine Ledent, Marius Kloft

A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings Junhyung Park, Krikamol Muandet

Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality Nian Si, Jose Blanchet, Soumyadip Ghosh, Mark Squillante

Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, koray kavukcuoglu, Remi Munos, Michal Valko

Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning Pan Zhou, Jiashi Feng, Chao Ma, Caiming Xiong, Steven Chu Hong Hoi, Weinan E

RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor Fan Lu, Guang Chen, Yinlong Liu, Zhongnan Qu, Alois Knoll

Efficient Clustering for Stretched Mixtures: Landscape and Optimality Kaizheng Wang, Yuling Yan, Mateo Diaz

A Group-Theoretic Framework for Data Augmentation Shuxiao Chen, Edgar Dobriban, Jane Lee

The Statistical Cost of Robust Kernel Hyperparameter Turning Raphael Meyer, Christopher Musco

How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks? Gaojie Jin, Xinping Yi, Liang Zhang, Lijun Zhang, Sven Schewe, Xiaowei Huang

ContraGAN: Contrastive Learning for Conditional Image Generation Minguk Kang, Jaesik Park

On the distance between two neural networks and the stability of learning Jeremy Bernstein, Arash Vahdat, Yisong Yue, Ming-Yu Liu

A Topological Filter for Learning with Label Noise Pengxiang Wu, Songzhu Zheng, Mayank Goswami, Dimitris Metaxas, Chao Chen

Personalized Federated Learning with Moreau Envelopes Canh T. Dinh, Nguyen Tran, Josh Nguyen

Avoiding Side Effects in Complex Environments Alex Turner, Neale Ratzlaff, Prasad Tadepalli

No-regret Learning in Price Competitions under Consumer Reference Effects Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang

Geometric Dataset Distances via Optimal Transport David Alvarez-Melis, Nicolo Fusi

Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters Sulin Liu, Xingyuan Sun, Peter J. Ramadge, Ryan P. Adams

A novel variational form of the Schatten-$p$ quasi-norm Paris Giampouras, Rene Vidal, Athanasios Rontogiannis, Benjamin Haeffele

Energy-based Out-of-distribution Detection Weitang Liu, Xiaoyun Wang, John Owens, Yixuan Li

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk

User-Dependent Neural Sequence Models for Continuous-Time Event Data Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth

Active Structure Learning of Causal DAGs via Directed Clique Trees Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam

Convergence and Stability of Graph Convolutional Networks on Large Random Graphs Nicolas Keriven, Alberto Bietti, Samuel Vaiter

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham, Andrew G. Wilson, Eytan Bakshy

Reconsidering Generative Objectives For Counterfactual Reasoning Danni Lu, Chenyang Tao, Junya Chen, Fan Li, Feng Guo, Lawrence Carin

Robust Federated Learning: The Case of Affine Distribution Shifts Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, Ali Jadbabaie

Quantile Propagation for Wasserstein-Approximate Gaussian Processes Rui Zhang, Christian Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie

Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning Tianren Zhang, Shangqi Guo, Tian Tan, Xiaolin Hu, Feng Chen

High-contrast “gaudy” images improve the training of deep neural network models of visual cortex Benjamin Cowley, Jonathan W. Pillow

Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion Zhanqiu Zhang, Jianyu Cai, Jie Wang

Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms Xiangyi Chen, Tiancong Chen, Haoran Sun, Steven Z. Wu, Mingyi Hong

H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks Thomas Limbacher, Robert Legenstein

Neural Unsigned Distance Fields for Implicit Function Learning Julian Chibane, Mohamad Aymen mir, Gerard Pons-Moll

Curriculum By Smoothing Samarth Sinha, Animesh Garg, Hugo Larochelle

Fast Transformers with Clustered Attention Apoorv Vyas, Angelos Katharopoulos, François Fleuret

The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification Christian Tjandraatmadja, Ross Anderson, Joey Huchette, Will Ma, KRUNAL KISHOR PATEL, Juan Pablo Vielma

Strongly Incremental Constituency Parsing with Graph Neural Networks Kaiyu Yang, Jia Deng

AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection Hao Zhu, Chaoyou Fu, Qianyi Wu, Wayne Wu, Chen Qian, Ran He

Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation Ping Hu, Stan Sclaroff, Kate Saenko

Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians Juhan Bae, Roger B. Grosse

First-Order Methods for Large-Scale Market Equilibrium Computation Yuan Gao, Christian Kroer

Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects Zijun Gao, Yanjun Han

Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis Ye Yuan, Kris Kitani

A General Method for Robust Learning from Batches Ayush Jain, Alon Orlitsky

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning Zhongzheng Ren, Raymond Yeh, Alexander Schwing

Hard Negative Mixing for Contrastive Learning Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, Diane Larlus

MOReL: Model-Based Offline Reinforcement Learning Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims

Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings Christopher Morris, Gaurav Rattan, Petra Mutzel

Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion Qianqian Ma, Alex Olshevsky

Learning Semantic-aware Normalization for Generative Adversarial Networks Heliang Zheng, Jianlong Fu, Yanhong Zeng, Jiebo Luo, Zheng-Jun Zha

Differentiable Causal Discovery from Interventional Data Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin

One-sample Guided Object Representation Disassembling Zunlei Feng, Yongming He, Xinchao Wang, Xin Gao, Jie Lei, Cheng Jin, Mingli Song

Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate Akifumi Okuno, Hidetoshi Shimodaira

Robust Persistence Diagrams using Reproducing Kernels Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath K. Sriperumbudur

Contextual Games: Multi-Agent Learning with Side Information Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause, Maryam Kamgarpour

Goal-directed Generation of Discrete Structures with Conditional Generative Models Amina Mollaysa, Brooks Paige, Alexandros Kalousis

Beyond Lazy Training for Over-parameterized Tensor Decomposition Xiang Wang, Chenwei Wu, Jason D. Lee, Tengyu Ma, Rong Ge

Denoised Smoothing: A Provable Defense for Pretrained Classifiers Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, J. Zico Kolter

Minibatch Stochastic Approximate Proximal Point Methods Hilal Asi, Karan Chadha, Gary Cheng, John C. Duchi

Attribute Prototype Network for Zero-Shot Learning Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata

CrossTransformers: spatially-aware few-shot transfer Carl Doersch, Ankush Gupta, Andrew Zisserman

Learning Latent Space Energy-Based Prior Model Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu

SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology Mark Veillette, Siddharth Samsi, Chris Mattioli

Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation Bowen Li, Xiaojuan Qi, Philip Torr, Thomas Lukasiewicz

High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization Qing Feng , Ben Letham, Hongzi Mao, Eytan Bakshy

Model Fusion via Optimal Transport Sidak Pal Singh, Martin Jaggi

On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems Kaiqing Zhang, Bin Hu, Tamer Basar

Learning Individually Inferred Communication for Multi-Agent Cooperation Ziluo Ding, Tiejun Huang, Zongqing Lu

Set2Graph: Learning Graphs From Sets Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman

Graph Random Neural Networks for Semi-Supervised Learning on Graphs Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang

Gradient Boosted Normalizing Flows Robert Giaquinto, Arindam Banerjee

Open Graph Benchmark: Datasets for Machine Learning on Graphs Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec

Towards Understanding Hierarchical Learning: Benefits of Neural Representations Minshuo Chen, Yu Bai, Jason D. Lee, Tuo Zhao, Huan Wang, Caiming Xiong, Richard Socher

Texture Interpolation for Probing Visual Perception Jonathan Vacher, Aida Davila, Adam Kohn, Ruben Coen-Cagli

Hierarchical Neural Architecture Search for Deep Stereo Matching Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang, Hongdong Li, Tom Drummond, Zongyuan Ge

MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models Sourav Biswas, Jerry Liu, Kelvin Wong, Shenlong Wang, Raquel Urtasun

Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy Edward Moroshko, Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Nati Srebro, Daniel Soudry

Focus of Attention Improves Information Transfer in Visual Features Matteo Tiezzi, Stefano Melacci, Alessandro Betti, Marco Maggini, Marco Gori

Auditing Differentially Private Machine Learning: How Private is Private SGD? Matthew Jagielski, Jonathan Ullman, Alina Oprea

A Dynamical Central Limit Theorem for Shallow Neural Networks Zhengdao Chen, Grant Rotskoff, Joan Bruna, Eric Vanden-Eijnden

Measuring Systematic Generalization in Neural Proof Generation with Transformers Nicolas Gontier, Koustuv Sinha, Siva Reddy, Chris Pal

Big Self-Supervised Models are Strong Semi-Supervised Learners Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton

Learning from Label Proportions: A Mutual Contamination Framework Clayton Scott, Jianxin Zhang

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob Gardner

Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory Slabaugh, Qi Tian

Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning Nathan Kallus, Angela Zhou

Model Class Reliance for Random Forests Gavin Smith, Roberto Mansilla, James Goulding

Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games Arun Suggala, Praneeth Netrapalli

Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity Simon S. Du, Jason D. Lee, Gaurav Mahajan, Ruosong Wang

Learning to Adapt to Evolving Domains Hong Liu, Mingsheng Long, Jianmin Wang, Yu Wang

Synthesizing Tasks for Block-based Programming Umair Ahmed, Maria Christakis, Aleksandr Efremov, Nigel Fernandez, Ahana Ghosh, Abhik Roychoudhury, Adish Singla

Scalable Belief Propagation via Relaxed Scheduling Vitalii Aksenov, Dan Alistarh, Janne H. Korhonen

Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu

Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang, Qiaomin Xie

Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes Salman Habib, Allison Beemer, Joerg Kliewer

Faster DBSCAN via subsampled similarity queries Heinrich Jiang, Jennifer Jang, Jakub Lacki

De-Anonymizing Text by Fingerprinting Language Generation Zhen Sun, Roei Schuster, Vitaly Shmatikov

Multiparameter Persistence Image for Topological Machine Learning Mathieu Carrière, Andrew Blumberg

PLANS: Neuro-Symbolic Program Learning from Videos Raphaël Dang-Nhu

Matrix Inference and Estimation in Multi-Layer Models Parthe Pandit, Mojtaba Sahraee Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher

MeshSDF: Differentiable Iso-Surface Extraction Edoardo Remelli, Artem Lukoianov, Stephan Richter, Benoit Guillard, Timur Bagautdinov, Pierre Baque, Pascal Fua

Variational Interaction Information Maximization for Cross-domain Disentanglement HyeongJoo Hwang, Geon-Hyeong Kim, Seunghoon Hong, Kee-Eung Kim

Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning Fei Feng, Ruosong Wang, Wotao Yin, Simon S. Du, Lin Yang

Faithful Embeddings for Knowledge Base Queries Haitian Sun, Andrew Arnold, Tania Bedrax Weiss, Fernando Pereira, William W. Cohen

Wasserstein Distances for Stereo Disparity Estimation Divyansh Garg, Yan Wang, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger, Wei-Lun Chao

Multi-agent Trajectory Prediction with Fuzzy Query Attention Nitin Kamra, Hao Zhu, Dweep Kumarbhai Trivedi, Ming Zhang, Yan Liu

Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping Shashanka Ubaru, Sanjeeb Dash, Arya Mazumdar, Oktay Gunluk

An Analysis of SVD for Deep Rotation Estimation Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia

Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu, Rafal Bogacz

Manifold GPLVMs for discovering non-Euclidean latent structure in neural data Kristopher Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin

Distributed Distillation for On-Device Learning Ilai Bistritz, Ariana Mann, Nicholas Bambos

COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning Simon Ging, Mohammadreza Zolfaghari, Hamed Pirsiavash, Thomas Brox

Passport-aware Normalization for Deep Model Protection Jie Zhang, Dongdong Chen, Jing Liao, Weiming Zhang, Gang Hua, Nenghai Yu

Sampling-Decomposable Generative Adversarial Recommender Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma, Xing Xie, Enhong Chen

Limits to Depth Efficiencies of Self-Attention Yoav Levine, Noam Wies, Or Sharir, Hofit Bata, Amnon Shashua

Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

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