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ORCID36th ICML 2019: Long Beach, California, USA
- Kamalika Chaudhuri, Ruslan Salakhutdinov:
Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research 97, PMLR 2019 - Gabriele Abbati, Philippe Wenk, Michael A. Osborne
, Andreas Krause, Bernhard Schölkopf, Stefan Bauer:
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs. 1-10 - Axel Abels, Diederik M. Roijers, Tom Lenaerts, Ann Nowé, Denis Steckelmacher:
Dynamic Weights in Multi-Objective Deep Reinforcement Learning. 11-20 - Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan:
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. 21-29 - Jayadev Acharya, Clément L. Canonne, Himanshu Tyagi:
Communication-Constrained Inference and the Role of Shared Randomness. 30-39 - Jayadev Acharya, Chris De Sa, Dylan J. Foster, Karthik Sridharan:
Distributed Learning with Sublinear Communication. 40-50 - Jayadev Acharya, Ziteng Sun:
Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters. 51-60 - Roy Adams, Yuelong Ji, Xiaobin Wang, Suchi Saria:
Learning Models from Data with Measurement Error: Tackling Underreporting. 61-70 - Tameem Adel, Adrian Weller:
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning. 71-81 - Abhijin Adiga, Chris J. Kuhlman, Madhav V. Marathe, S. S. Ravi, Anil Vullikanti:
PAC Learnability of Node Functions in Networked Dynamical Systems. 82-91 - Ashish Agarwal:
Static Automatic Batching In TensorFlow. 92-101 - Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang:
Efficient Full-Matrix Adaptive Regularization. 102-110 - Naman Agarwal, Brian Bullins, Elad Hazan, Sham M. Kakade, Karan Singh:
Online Control with Adversarial Disturbances. 111-119 - Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu:
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms. 120-129 - Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi:
Learning to Generalize from Sparse and Underspecified Rewards. 130-140 - Raj Agrawal, Brian L. Trippe, Jonathan H. Huggins, Tamara Broderick:
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions. 141-150 - Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans:
Understanding the Impact of Entropy on Policy Optimization. 151-160 - Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara, Alain Tapp:
Fairwashing: the risk of rationalization. 161-170 - Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, Kouhei Nishida:
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search. 171-180 - Riad Akrour, Joni Pajarinen, Jan Peters, Gerhard Neumann:
Projections for Approximate Policy Iteration Algorithms. 181-190 - Ahmed M. Alaa, Mihaela van der Schaar:
Validating Causal Inference Models via Influence Functions. 191-201 - Isabela Albuquerque, João Monteiro, Thang Doan, Breandan Considine, Tiago H. Falk, Ioannis Mitliagkas:
Multi-objective training of Generative Adversarial Networks with multiple discriminators. 202-211 - Ferran Alet, Adarsh Keshav Jeewajee, Maria Bauzá Villalonga, Alberto Rodriguez, Tomás Lozano-Pérez, Leslie Pack Kaelbling:
Graph Element Networks: adaptive, structured computation and memory. 212-222 - Carl Allen, Timothy M. Hospedales:
Analogies Explained: Towards Understanding Word Embeddings. 223-231 - Kelsey R. Allen, Evan Shelhamer, Hanul Shin, Joshua B. Tenenbaum:
Infinite Mixture Prototypes for Few-shot Learning. 232-241 - Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song:
A Convergence Theory for Deep Learning via Over-Parameterization. 242-252 - Ahsan S. Alvi, Bin Xin Ru, Jan-Peter Calliess, Stephen J. Roberts, Michael A. Osborne:
Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation. 253-262 - Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii:
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy. 263-271 - Marco Ancona, Cengiz Öztireli, Markus H. Gross:
Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation. 272-281 - Jesse Anderton, Javed A. Aslam:
Scaling Up Ordinal Embedding: A Landmark Approach. 282-290 - Cem Anil, James Lucas, Roger B. Grosse:
Sorting Out Lipschitz Function Approximation. 291-301 - Luigi Antelmi, Nicholas Ayache, Philippe Robert, Marco Lorenzi:
Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data. 302-311 - Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, Kevin McGuinness:
Unsupervised Label Noise Modeling and Loss Correction. 312-321 - Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang:
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks. 322-332 - Sepehr Assadi, MohammadHossein Bateni, Vahab S. Mirrokni:
Distributed Weighted Matching via Randomized Composable Coresets. 333-343 - Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Michael G. Rabbat:
Stochastic Gradient Push for Distributed Deep Learning. 344-353 - Raul Astudillo, Peter I. Frazier:
Bayesian Optimization of Composite Functions. 354-363 - Kubilay Atasu, Thomas Mittelholzer:
Linear-Complexity Data-Parallel Earth Mover's Distance Approximations. 364-373 - Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra B. Slavkovic:
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA. 374-384 - Sergül Aydöre, Bertrand Thirion, Gaël Varoquaux:
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data. 385-394 - Fadhel Ayed, Juho Lee, Francois Caron:
Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior. 395-404 - Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian, Tal Wagner:
Scalable Fair Clustering. 405-413 - Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi:
Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs. 414-423 - Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar:
Provable Guarantees for Gradient-Based Meta-Learning. 424-433 - David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki, Julien Pérolat, Max Jaderberg, Thore Graepel:
Open-ended learning in symmetric zero-sum games. 434-443 - Muhammed Fatih Balin, Abubakar Abid, James Y. Zou:
Concrete Autoencoders: Differentiable Feature Selection and Reconstruction. 444-453 - Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, Christian Szegedy, Stewart Wilcox:
HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving. 454-463 - Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick:
Structured agents for physical construction. 464-474 - Dmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin, Artem Babenko:
Learning to Route in Similarity Graphs. 475-484 - Pablo V. A. Barros, German Ignacio Parisi, Stefan Wermter:
A Personalized Affective Memory Model for Improving Emotion Recognition. 485-494 - Peter L. Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko:
Scale-free adaptive planning for deterministic dynamics & discounted rewards. 495-504 - Soumya Basu, Steven Gutstein, Brent Lance, Sanjay Shakkottai:
Pareto Optimal Streaming Unsupervised Classification. 505-514 - MohammadHossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab S. Mirrokni, Afshin Rostamizadeh:
Categorical Feature Compression via Submodular Optimization. 515-523 - Joshua Batson, Loïc Royer:
Noise2Self: Blind Denoising by Self-Supervision. 524-533 - Alex Beatson, Ryan P. Adams:
Efficient optimization of loops and limits with randomized telescoping sums. 534-543 - Philipp Becker, Harit Pandya, Gregor H. W. Gebhardt, Cheng Zhao, C. James Taylor, Gerhard Neumann:
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces. 544-552 - Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt:
Switching Linear Dynamics for Variational Bayes Filtering. 553-562 - Sima Behpour, Anqi Liu, Brian D. Ziebart:
Active Learning for Probabilistic Structured Prediction of Cuts and Matchings. 563-572 - Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen:
Invertible Residual Networks. 573-582 - Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon:
Greedy Layerwise Learning Can Scale To ImageNet. 583-593 - Yassine Benyahia, Kaicheng Yu, Kamil Bennani-Smires, Martin Jaggi, Anthony C. Davison, Mathieu Salzmann, Claudiu Musat:
Overcoming Multi-model Forgetting. 594-603 - Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger:
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning. 604-613 - Martín Bertrán, Natalia Martínez, Afroditi Papadaki, Qiang Qiu, Miguel R. D. Rodrigues, Galen Reeves, Guillermo Sapiro:
Adversarially Learned Representations for Information Obfuscation and Inference. 614-623 - Alina Beygelzimer, Dávid Pál, Balázs Szörényi, Devanathan Thiruvenkatachari, Chen-Yu Wei, Chicheng Zhang:
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case. 624-633 - Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin B. Calo:
Analyzing Federated Learning through an Adversarial Lens. 634-643 - Yatao An Bian, Joachim M. Buhmann, Andreas Krause:
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference. 644-653 - Aurélien Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan:
More Efficient Off-Policy Evaluation through Regularized Targeted Learning. 654-663 - Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal:
A Kernel Perspective for Regularizing Deep Neural Networks. 664-674 - Yochai Blau, Tomer Michaeli:
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff. 675-685 - Vinay Praneeth Boda, Prashanth L. A.:
Correlated bandits or: How to minimize mean-squared error online. 686-694 - Aleksandar Bojchevski, Stephan Günnemann:
Adversarial Attacks on Node Embeddings via Graph Poisoning. 695-704 - Zalán Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause:
Online Variance Reduction with Mixtures. 705-714 - Avishek Joey Bose, William L. Hamilton:
Compositional Fairness Constraints for Graph Embeddings. 715-724 - Xavier Bouthillier, César Laurent, Pascal Vincent:
Unreproducible Research is Reproducible. 725-734 - Gábor Braun, Sebastian Pokutta, Dan Tu, Stephen J. Wright:
Blended Conditonal Gradients. 735-743 - Vladimir Braverman, Shaofeng H.-C. Jiang, Robert Krauthgamer, Xuan Wu:
Coresets for Ordered Weighted Clustering. 744-753 - Margaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz:
Target Tracking for Contextual Bandits: Application to Demand Side Management. 754-763 - Robert A. Bridges, Anthony D. Gruber, Christopher Felder, Miki E. Verma, Chelsey Hoff:
Active Manifolds: A non-linear analogue to Active Subspaces. 764-772 - David H. Brookes, Hahnbeom Park, Jennifer Listgarten:
Conditioning by adaptive sampling for robust design. 773-782 - Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum:
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. 783-792 - Noam Brown, Adam Lerer, Sam Gross, Tuomas Sandholm:
Deep Counterfactual Regret Minimization. 793-802 - Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard S. Zemel:
Understanding the Origins of Bias in Word Embeddings. 803-811 - Alon Brutzkus, Ran Gilad-Bachrach, Oren Elisha:
Low Latency Privacy Preserving Inference. 812-821 - Alon Brutzkus, Amir Globerson:
Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem. 822-830 - Sébastien Bubeck, Yin Tat Lee, Eric Price, Ilya P. Razenshteyn:
Adversarial examples from computational constraints. 831-840 - Eliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias:
Self-similar Epochs: Value in arrangement. 841-850 - Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka:
Learning Generative Models across Incomparable Spaces. 851-861 - David R. Burt, Carl Edward Rasmussen, Mark van der Wilk:
Rates of Convergence for Sparse Variational Gaussian Process Regression. 862-871 - Jonathon Byrd, Zachary Chase Lipton:
What is the Effect of Importance Weighting in Deep Learning? 872-881 - Yongqiang Cai, Qianxiao Li, Zuowei Shen:
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent. 882-890 - Bugra Can, Mert Gürbüzbalaban, Lingjiong Zhu:
Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances. 891-901 - Gregory Canal, Andrew K. Massimino, Mark A. Davenport, Christopher J. Rozell:
Active Embedding Search via Noisy Paired Comparisons. 902-911 - Junyu Cao, Wei Sun:
Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem. 912-920 - Adrian Rivera Cardoso, Jacob D. Abernethy, He Wang, Huan Xu:
Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games. 921-930 - Henry Chai, Jean-Francois Ton, Michael A. Osborne, Roman Garnett:
Automated Model Selection with Bayesian Quadrature. 931-940 - Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas:
Learning Action Representations for Reinforcement Learning. 941-950 - Chun-Hao Chang, Mingjie Mai, Anna Goldenberg:
Dynamic Measurement Scheduling for Event Forecasting using Deep RL. 951-960 - Nontawat Charoenphakdee, Jongyeong Lee, Masashi Sugiyama:
On Symmetric Losses for Learning from Corrupted Labels. 961-970 - Niladri S. Chatterji, Aldo Pacchiano, Peter L. Bartlett:
Online learning with kernel losses. 971-980 - Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N. Balasubramanian:
Neural Network Attributions: A Causal Perspective. 981-990 - Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan:
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits. 991-1000 - George H. Chen:
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates. 1001-1010 - Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark A. Girolami, Lester W. Mackey, Chris J. Oates:
Stein Point Markov Chain Monte Carlo. 1011-1021 - Xinshi Chen, Hanjun Dai, Le Song:
Particle Flow Bayes' Rule. 1022-1031 - Xingyu Chen, Brandon Fain, Liang Lyu, Kamesh Munagala:
Proportionally Fair Clustering. 1032-1041 - Jinglin Chen, Nan Jiang:
Information-Theoretic Considerations in Batch Reinforcement Learning. 1042-1051 - Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, Le Song:
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System. 1052-1061 - Pengfei Chen, Benben Liao, Guangyong Chen, Shengyu Zhang:
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels. 1062-1070 - Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng:
A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization. 1071-1080 - Xinyang Chen, Sinan Wang, Mingsheng Long, Jianmin Wang:
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation. 1081-1090 - Pin-Yu Chen, Lingfei Wu, Sijia Liu, Indika Rajapakse:
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications. 1091-1101 - Zaiyi Chen, Yi Xu, Haoyuan Hu, Tianbao Yang:
Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number. 1102-1111 - Ziliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan, Guanbin Li, Liang Lin:
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching. 1112-1121 - Hongge Chen, Huan Zhang, Duane S. Boning, Cho-Jui Hsieh:
Robust Decision Trees Against Adversarial Examples. 1122-1131 - Xiaoshuang Chen, Yin Zheng, Jiaxing Wang, Wenye Ma, Junzhou Huang:
RaFM: Rank-Aware Factorization Machines. 1132-1140 - Richard Cheng, Abhinav Verma, Gábor Orosz, Swarat Chaudhuri, Yisong Yue, Joel Burdick:
Control Regularization for Reduced Variance Reinforcement Learning. 1141-1150 - Ching-An Cheng, Xinyan Yan, Nathan D. Ratliff, Byron Boots:
Predictor-Corrector Policy Optimization. 1151-1161 - Julien Chiquet, Stéphane Robin, Mahendra Mariadassou:
Variational Inference for sparse network reconstruction from count data. 1162-1171 - Uthsav Chitra, Benjamin J. Raphael:
Random Walks on Hypergraphs with Edge-Dependent Vertex Weights. 1172-1181 - Kristy Choi, Kedar Tatwawadi, Aditya Grover, Tsachy Weissman, Stefano Ermon:
Neural Joint Source-Channel Coding. 1182-1192 - Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Paolo Diachille, Viatcheslav Gurev, Brian Kingsbury, Ravi Tejwani, Djallel Bouneffouf:
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables. 1193-1202 - Krzysztof Choromanski, Mark Rowland, Wenyu Chen, Adrian Weller:
Unifying Orthogonal Monte Carlo Methods. 1203-1212 - Casey Chu, Jose H. Blanchet, Peter W. Glynn:
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning. 1213-1222 - Eric Chu, Peter J. Liu:
MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization. 1223-1232 - Hye Won Chung, Ji Oon Lee:
Weak Detection of Signal in the Spiked Wigner Model. 1233-1241 - Ferdinando Cicalese, Eduardo Sany Laber, Lucas Murtinho:
New results on information theoretic clustering. 1242-1251 - Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim:
Sensitivity Analysis of Linear Structural Causal Models. 1252-1261 - Kenneth L. Clarkson, Ruosong Wang, David P. Woodruff:
Dimensionality Reduction for Tukey Regression. 1262-1271 - Stéphan Clémençon, Pierre Laforgue, Patrice Bertail:
On Medians of (Randomized) Pairwise Means. 1272-1281 - Karl Cobbe, Oleg Klimov, Christopher Hesse, Taehoon Kim, John Schulman:
Quantifying Generalization in Reinforcement Learning. 1282-1289 - Eldan Cohen, J. Christopher Beck:
Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models. 1290-1299 - Alon Cohen, Tomer Koren, Yishay Mansour:
Learning Linear-Quadratic Regulators Efficiently with only √T Regret. 1300-1309 - Jeremy Cohen, Elan Rosenfeld, J. Zico Kolter:
Certified Adversarial Robustness via Randomized Smoothing. 1310-1320 - Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling:
Gauge Equivariant Convolutional Networks and the Icosahedral CNN. 1321-1330 - Cédric Colas, Pierre-Yves Oudeyer, Olivier Sigaud, Pierre Fournier, Mohamed Chetouani:
CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning. 1331-1340 - Ronan Collobert, Awni Y. Hannun, Gabriel Synnaeve:
A fully differentiable beam search decoder. 1341-1350 - Robert Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet:
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. 1351-1360 - Juan D. Correa, Jin Tian, Elias Bareinboim:
Adjustment Criteria for Generalizing Experimental Findings. 1361-1369 - Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang:
Online Learning with Sleeping Experts and Feedback Graphs. 1370-1378 - Corinna Cortes, Giulia DeSalvo, Mehryar Mohri, Ningshan Zhang, Claudio Gentile:
Active Learning with Disagreement Graphs. 1379-1387 - Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Erez Louidor, James Muller, Taman Narayan, Serena Lutong Wang, Tao Zhu:
Shape Constraints for Set Functions. 1388-1396 - Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You:
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. 1397-1405 - Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder:
Monge blunts Bayes: Hardness Results for Adversarial Training. 1406-1415 - Zac Cranko, Richard Nock:
Boosted Density Estimation Remastered. 1416-1425 - Victoria G. Crawford, Alan Kuhnle, My T. Thai:
Submodular Cost Submodular Cover with an Approximate Oracle. 1426-1435 - Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard S. Zemel:
Flexibly Fair Representation Learning by Disentanglement. 1436-1445 - Ashok Cutkosky:
Anytime Online-to-Batch, Optimism and Acceleration. 1446-1454 - Ashok Cutkosky, Tamás Sarlós:
Matrix-Free Preconditioning in Online Learning. 1455-1464 - Milan Cvitkovic, Günther Koliander:
Minimal Achievable Sufficient Statistic Learning. 1465-1474 - Milan Cvitkovic, Badal Singh, Animashree Anandkumar:
Open Vocabulary Learning on Source Code with a Graph-Structured Cache. 1475-1485 - Robert Dadashi, Marc G. Bellemare, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans:
The Value Function Polytope in Reinforcement Learning. 1486-1495 - Zhongxiang Dai, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet:
Bayesian Optimization Meets Bayesian Optimal Stopping. 1496-1506 - Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill:
Policy Certificates: Towards Accountable Reinforcement Learning. 1507-1516 - Tri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Ré:
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations. 1517-1527 - Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Ré:
A Kernel Theory of Modern Data Augmentation. 1528-1537 - Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Mike Rabbat, Joelle Pineau:
TarMAC: Targeted Multi-Agent Communication. 1538-1546 - Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu:
Teaching a black-box learner. 1547-1555 - Gwendoline de Bie, Gabriel Peyré, Marco Cuturi:
Stochastic Deep Networks. 1556-1565 - Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil:
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization. 1566-1575 - Onur Dereli, Ceyda Oguz, Mehmet Gönen:
A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology. 1576-1585 - Nichita Diaconu, Daniel E. Worrall:
Learning to Convolve: A Generalized Weight-Tying Approach. 1586-1595 - Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart:
Sever: A Robust Meta-Algorithm for Stochastic Optimization. 1596-1606 - Xiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han, Chenggang Yan:
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization. 1607-1616 - Tianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Daniel P. Robinson, Manolis C. Tsakiris, René Vidal:
Noisy Dual Principal Component Pursuit. 1617-1625 - Thinh T. Doan, Siva Theja Maguluri, Justin Romberg:
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning. 1626-1635 - Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel:
Trajectory-Based Off-Policy Deep Reinforcement Learning. 1636-1645 - Elvis Dohmatob:
Generalized No Free Lunch Theorem for Adversarial Robustness. 1646-1654 - Simon S. Du, Wei Hu:
Width Provably Matters in Optimization for Deep Linear Neural Networks. 1655-1664 - Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford:
Provably efficient RL with Rich Observations via Latent State Decoding. 1665-1674 - Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, Xiyu Zhai:
Gradient Descent Finds Global Minima of Deep Neural Networks. 1675-1685 - Junliang Du, Antonio R. Linero:
Incorporating Grouping Information into Bayesian Decision Tree Ensembles. 1686-1695 - Yilun Du, Karthik Narasimhan:
Task-Agnostic Dynamics Priors for Deep Reinforcement Learning. 1696-1705 - Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, Sai Srivatsa Ravindranath:
Optimal Auctions through Deep Learning. 1706-1715 - Yonatan Dukler, Wuchen Li, Alex Tong Lin, Guido Montúfar:
Wasserstein of Wasserstein Loss for Learning Generative Models. 1716-1725 - Lea Duncker, Gergo Bohner, Julien Boussard, Maneesh Sahani:
Learning interpretable continuous-time models of latent stochastic dynamical systems. 1726-1734 - Conor Durkan, Charlie Nash:
Autoregressive Energy Machines. 1735-1744 - Adam Dziedzic, John Paparrizos, Sanjay Krishnan, Aaron J. Elmore, Michael J. Franklin:
Band-limited Training and Inference for Convolutional Neural Networks. 1745-1754 - Ashley D. Edwards, Himanshu Sahni, Yannick Schroecker, Charles L. Isbell Jr.:
Imitating Latent Policies from Observation. 1755-1763 - Hubert Eichner, Tomer Koren, Brendan McMahan, Nathan Srebro, Kunal Talwar:
Semi-Cyclic Stochastic Gradient Descent. 1764-1773 - Mohamed Elfeki, Camille Couprie, Morgane Rivière, Mohamed Elhoseiny:
GDPP: Learning Diverse Generations using Determinantal Point Processes. 1774-1783 - Ehsan Elhamifar:
Sequential Facility Location: Approximate Submodularity and Greedy Algorithm. 1784-1793 - Alina Ene, Adrian Vladu:
Improved Convergence for $\ell_1$ and $\ell_∞$ Regression via Iteratively Reweighted Least Squares. 1794-1801 - Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry:
Exploring the Landscape of Spatial Robustness. 1802-1811 - Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia:
Cross-Domain 3D Equivariant Image Embeddings. 1812-1822 - Christian Etmann, Sebastian Lunz, Peter Maass, Carola Schönlieb:
On the Connection Between Adversarial Robustness and Saliency Map Interpretability. 1823-1832 - Matthew Fahrbach, Vahab S. Mirrokni, Morteza Zadimoghaddam:
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity. 1833-1842 - Yifeng Fan, Zhizhen Zhao:
Multi-Frequency Vector Diffusion Maps. 1843-1852 - Gabriele Farina, Christian Kroer, Noam Brown, Tuomas Sandholm:
Stable-Predictive Optimistic Counterfactual Regret Minimization. 1853-1862 - Gabriele Farina, Christian Kroer, Tuomas Sandholm:
Regret Circuits: Composability of Regret Minimizers. 1863-1872 - Mehdi Fatemi, Shikhar Sharma, Harm van Seijen, Samira Ebrahimi Kahou:
Dead-ends and Secure Exploration in Reinforcement Learning. 1873-1881 - Ilya Feige:
Invariant-Equivariant Representation Learning for Multi-Class Data. 1882-1891 - Vitaly Feldman, Roy Frostig, Moritz Hardt:
The advantages of multiple classes for reducing overfitting from test set reuse. 1892-1900 - Raphaël Féraud, Réda Alami, Romain Laroche:
Decentralized Exploration in Multi-Armed Bandits. 1901-1909 - Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara, Volkan Cevher:
Almost surely constrained convex optimization. 1910-1919 - Chelsea Finn, Aravind Rajeswaran, Sham M. Kakade, Sergey Levine:
Online Meta-Learning. 1920-1930 - Marc Fischer, Mislav Balunovic, Dana Drachsler-Cohen, Timon Gehr, Ce Zhang, Martin T. Vechev:
DL2: Training and Querying Neural Networks with Logic. 1931-1941 - Jakob N. Foerster, H. Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew M. Botvinick, Michael Bowling:
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. 1942-1951 - Edwin Fong, Simon Lyddon, Chris C. Holmes:
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap. 1952-1962 - Vojtech Franc, Daniel Prusa:
On discriminative learning of prediction uncertainty. 1963-1971 - Luca Franceschi, Mathias Niepert, Massimiliano Pontil, Xiao He:
Learning Discrete Structures for Graph Neural Networks. 1972-1982 - Dror Freirich, Tzahi Shimkin, Ron Meir, Aviv Tamar:
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN. 1983-1992 - Thomas Frerix, Joan Bruna:
Approximating Orthogonal Matrices with Effective Givens Factorization. 1993-2001 - Charlie Frogner, Tomaso A. Poggio:
Fast and Flexible Inference of Joint Distributions from their Marginals. 2002-2011 - Nicholas Frosst, Nicolas Papernot, Geoffrey E. Hinton:
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss. 2012-2020 - Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine:
Diagnosing Bottlenecks in Deep Q-learning Algorithms. 2021-2030 - Szu-Wei Fu, Chien-Feng Liao, Yu Tsao, Shou-De Lin:
MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement. 2031-2041 - Kaito Fujii, Shinsaku Sakaue:
Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio. 2042-2051 - Scott Fujimoto, David Meger, Doina Precup:
Off-Policy Deep Reinforcement Learning without Exploration. 2052-2062 - Shani Gamrian, Yoav Goldberg:
Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation. 2063-2072 - Octavian Ganea, Sylvain Gelly, Gary Bécigneul, Aliaksei Severyn:
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities. 2073-2082 - Hongyang Gao, Shuiwang Ji:
Graph U-Nets. 2083-2092 - Yuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang, Shunkang Zhang:
Deep Generative Learning via Variational Gradient Flow. 2093-2101 - Weihao Gao, Yu-Han Liu, Chong Wang, Sewoong Oh:
Rate Distortion For Model Compression: From Theory To Practice. 2102-2111 - Hongchang Gao, Jian Pei, Heng Huang:
Demystifying Dropout. 2112-2121 - Feng Gao, Guy Wolf, Matthew J. Hirn:
Geometric Scattering for Graph Data Analysis. 2122-2131 - Tingran Gao, Zhizhen Zhao:
Multi-Frequency Phase Synchronization. 2132-2141 - Nidham Gazagnadou, Robert M. Gower, Joseph Salmon:
Optimal Mini-Batch and Step Sizes for SAGA. 2142-2150 - Yonatan Geifman, Ran El-Yaniv:
SelectiveNet: A Deep Neural Network with an Integrated Reject Option. 2151-2159 - Matthieu Geist, Bruno Scherrer, Olivier Pietquin:
A Theory of Regularized Markov Decision Processes. 2160-2169 - Carles Gelada, Saurabh Kumar, Jacob Buckman, Ofir Nachum, Marc G. Bellemare:
DeepMDP: Learning Continuous Latent Space Models for Representation Learning. 2170-2179 - Sinong Geng, Minhao Yan, Mladen Kolar, Sanmi Koyejo:
Partially Linear Additive Gaussian Graphical Models. 2180-2190 - Hossein Shokri Ghadikolaei, Hadi G. Ghauch, Carlo Fischione, Mikael Skoglund:
Learning and Data Selection in Big Datasets. 2191-2200 - Mohsen Ghaffari, Silvio Lattanzi, Slobodan Mitrovic:
Improved Parallel Algorithms for Density-Based Network Clustering. 2201-2210 - Badih Ghazi, Rina Panigrahy, Joshua R. Wang:
Recursive Sketches for Modular Deep Learning. 2211-2220 - Behrooz Ghorbani, Hamid Javadi, Andrea Montanari:
An Instability in Variational Inference for Topic Models. 2221-2231 - Behrooz Ghorbani, Shankar Krishnan, Ying Xiao:
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density. 2232-2241 - Amirata Ghorbani, James Y. Zou:
Data Shapley: Equitable Valuation of Data for Machine Learning. 2242-2251 - Dar Gilboa, Sam Buchanan, John Wright:
Efficient Dictionary Learning with Gradient Descent. 2252-2259 - Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvitskii:
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes. 2260-2268 - Jon Gillick, Adam Roberts, Jesse H. Engel, Douglas Eck, David Bamman:
Learning to Groove with Inverse Sequence Transformations. 2269-2279 - Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin D. Cubuk:
Adversarial Examples Are a Natural Consequence of Test Error in Noise. 2280-2289 - Jaime Roquero Gimenez, James Y. Zou:
Discovering Conditionally Salient Features with Statistical Guarantees. 2290-2298 - Ziv Goldfeld, Ewout van den Berg, Kristjan H. Greenewald, Igor Melnyk, Nam Nguyen, Brian Kingsbury, Yury Polyanskiy:
Estimating Information Flow in Deep Neural Networks. 2299-2308 - Adam Golinski, Frank Wood, Tom Rainforth:
Amortized Monte Carlo Integration. 2309-2318 - Sreenivas Gollapudi, Debmalya Panigrahi:
Online Algorithms for Rent-Or-Buy with Expert Advice. 2319-2327 - Alexander Golovnev, Dávid Pál, Balázs Szörényi:
The information-theoretic value of unlabeled data in semi-supervised learning. 2328-2336 - Linyuan Gong, Di He, Zhuohan Li, Tao Qin, Liwei Wang, Tie-Yan Liu:
Efficient Training of BERT by Progressively Stacking. 2337-2346 - ChengYue Gong, Jian Peng, Qiang Liu:
Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization. 2347-2356 - Paula Gordaliza, Eustasio del Barrio, Fabrice Gamboa, Jean-Michel Loubes:
Obtaining Fairness using Optimal Transport Theory. 2357-2365 - Omer Gottesman, Yao Liu, Scott Sussex, Emma Brunskill, Finale Doshi-Velez:
Combining parametric and nonparametric models for off-policy evaluation. 2366-2375 - Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, Stefan Lee:
Counterfactual Visual Explanations. 2376-2384 - James A. Grant, Alexis Boukouvalas, Ryan-Rhys Griffiths, David S. Leslie, Sattar Vakili, Enrique Munoz de Cote:
Adaptive Sensor Placement for Continuous Spaces. 2385-2393 - Alexander Greaves-Tunnell, Zaïd Harchaoui:
A Statistical Investigation of Long Memory in Language and Music. 2394-2403 - David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke:
Automatic Posterior Transformation for Likelihood-Free Inference. 2404-2414 - Daniel Greenfeld, Meirav Galun, Ronen Basri, Irad Yavneh, Ron Kimmel:
Learning to Optimize Multigrid PDE Solvers. 2415-2423 - Klaus Greff, Raphaël Lopez Kaufman, Rishabh Kabra, Nick Watters, Chris Burgess, Daniel Zoran, Loic Matthey, Matthew M. Botvinick, Alexander Lerchner:
Multi-Object Representation Learning with Iterative Variational Inference. 2424-2433 - Aditya Grover, Aaron Zweig, Stefano Ermon:
Graphite: Iterative Generative Modeling of Graphs. 2434-2444 - Jiaqi Gu, Guosheng Yin:
Fast Algorithm for Generalized Multinomial Models with Ranking Data. 2445-2453 - Chaoyu Guan, Xiting Wang, Quanshi Zhang, Runjin Chen, Di He, Xing Xie:
Towards a Deep and Unified Understanding of Deep Neural Models in NLP. 2454-2463 - Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Theophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy P. Lillicrap:
An Investigation of Model-Free Planning. 2464-2473 - Limor Gultchin, Genevieve Patterson, Nancy Baym, Nathaniel Swinger, Adam Kalai:
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops. 2474-2483 - Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger:
Simple Black-box Adversarial Attacks. 2484-2493 - Tian Guo, Tao Lin, Nino Antulov-Fantulin:
Exploring interpretable LSTM neural networks over multi-variable data. 2494-2504 - Lingbing Guo, Zequn Sun, Wei Hu:
Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs. 2505-2514 - Albert Gural, Boris Murmann:
Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications. 2515-2524 - Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto:
IMEXnet A Forward Stable Deep Neural Network. 2525-2534 - Guy Hacohen, Daphna Weinshall:
On The Power of Curriculum Learning in Training Deep Networks. 2535-2544 - Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi, Viveck R. Cadambe:
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization. 2545-2554 - Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson:
Learning Latent Dynamics for Planning from Pixels. 2555-2565 - Tavi Halperin, Ariel Ephrat, Yedid Hoshen:
Neural Separation of Observed and Unobserved Distributions. 2566-2575 - Lei Han, Peng Sun, Yali Du, Jiechao Xiong, Qing Wang, Xinghai Sun, Han Liu, Tong Zhang:
Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI. 2576-2585 - Seungyul Han, Youngchul Sung:
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning. 2586-2595 - Boris Hanin, David Rolnick:
Complexity of Linear Regions in Deep Networks. 2596-2604 - Josiah Hanna, Scott Niekum, Peter Stone:
Importance Sampling Policy Evaluation with an Estimated Behavior Policy. 2605-2613 - Yi Hao, Alon Orlitsky:
Doubly-Competitive Distribution Estimation. 2614-2623 - Jeff Z. HaoChen, Suvrit Sra:
Random Shuffling Beats SGD after Finite Epochs. 2624-2633 - Chris Harshaw, Moran Feldman, Justin Ward, Amin Karbasi:
Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications. 2634-2643 - Anna Harutyunyan, Peter Vrancx, Philippe Hamel, Ann Nowé, Doina Precup:
Per-Decision Option Discounting. 2644-2652 - Abolfazl Hashemi, Mahsa Ghasemi, Haris Vikalo, Ufuk Topcu:
Submodular Observation Selection and Information Gathering for Quadratic Models. 2653-2662 - Doron Haviv, Alexander Rivkind, Omri Barak:
Understanding and Controlling Memory in Recurrent Neural Networks. 2663-2671 - Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
On the Impact of the Activation function on Deep Neural Networks Training. 2672-2680 - Elad Hazan, Sham M. Kakade, Karan Singh, Abby Van Soest:
Provably Efficient Maximum Entropy Exploration. 2681-2691 - Hoda Heidari, Vedant Nanda, Krishna P. Gummadi:
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning. 2692-2701 - Julien M. Hendrickx, Alexander Olshevsky, Venkatesh Saligrama:
Graph Resistance and Learning from Pairwise Comparisons. 2702-2711 - Dan Hendrycks, Kimin Lee, Mantas Mazeika:
Using Pre-Training Can Improve Model Robustness and Uncertainty. 2712-2721 - Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel:
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design. 2722-2730 - Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, Pieter Abbeel:
Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules. 2731-2741 - Quang Minh Hoang, Trong Nghia Hoang, Bryan Kian Hsiang Low, Carl Kingsford:
Collective Model Fusion for Multiple Black-Box Experts. 2742-2750 - Christoph D. Hofer, Roland Kwitt, Marc Niethammer, Mandar Dixit:
Connectivity-Optimized Representation Learning via Persistent Homology. 2751-2760 - Matthew J. Holland, Kazushi Ikeda:
Better generalization with less data using robust gradient descent. 2761-2770 - Emiel Hoogeboom, Rianne van den Berg, Max Welling:
Emerging Convolutions for Generative Normalizing Flows. 2771-2780 - Samuel Horváth, Peter Richtárik:
Nonconvex Variance Reduced Optimization with Arbitrary Sampling. 2781-2789 - Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, Sylvain Gelly:
Parameter-Efficient Transfer Learning for NLP. 2790-2799 - Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya, P. R. Kumar:
Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging. 2800-2809 - Ya-Ping Hsieh, Chen Liu, Volkan Cevher:
Finding Mixed Nash Equilibria of Generative Adversarial Networks. 2810-2819 - Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama:
Classification from Positive, Unlabeled and Biased Negative Data. 2820-2829 - Kelvin Hsu, Fabio Ramos:
Bayesian Deconditional Kernel Mean Embeddings. 2830-2838 - Feihu Huang, Songcan Chen, Heng Huang:
Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization. 2839-2848 - Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu:
Unsupervised Deep Learning by Neighbourhood Discovery. 2849-2858 - Kejun Huang, Xiao Fu:
Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm. 2859-2868 - Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron C. Courville:
Hierarchical Importance Weighted Autoencoders. 2869-2878 - Lingxiao Huang, Nisheeth K. Vishnoi:
Stable and Fair Classification. 2879-2890 - Chen Huang, Shuangfei Zhai, Walter Talbott, Miguel Ángel Bautista, Shih-Yu Sun, Carlos Guestrin, Joshua M. Susskind:
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment. 2891-2900 - Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour:
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models. 2901-2910 - Jonathan J. Hunt, André Barreto, Timothy P. Lillicrap, Nicolas Heess:
Composing Entropic Policies using Divergence Correction. 2911-2920 - Uiwon Hwang, Dahuin Jung, Sungroh Yoon:
HexaGAN: Generative Adversarial Nets for Real World Classification. 2921-2930 - Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen:
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. 2931-2940 - Craig Innes, Alex Lascarides:
Learning Structured Decision Problems with Unawareness. 2941-2950 - Niels Bruun Ipsen, Lars Kai Hansen:
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size! 2951-2960 - Shariq Iqbal, Fei Sha:
Actor-Attention-Critic for Multi-Agent Reinforcement Learning. 2961-2970 - Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
Complementary-Label Learning for Arbitrary Losses and Models. 2971-2980 - Amin Jaber, Jiji Zhang, Elias Bareinboim:
Causal Identification under Markov Equivalence: Completeness Results. 2981-2989 - Alexis Jacq, Matthieu Geist, Ana Paiva, Olivier Pietquin:
Learning from a Learner. 2990-2999 - Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. 3000-3008 - Priyank Jaini, Kira A. Selby, Yaoliang Yu:
Sum-of-Squares Polynomial Flow. 3009-3018 - Jennifer Jang, Heinrich Jiang:
DBSCAN++: Towards fast and scalable density clustering. 3019-3029 - Yunhun Jang, Hankook Lee, Sung Ju Hwang, Jinwoo Shin:
Learning What and Where to Transfer. 3030-3039 - Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Çaglar Gülçehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas:
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning. 3040-3049 - Nathan Jay, Noga H. Rotman, Brighten Godfrey, Michael Schapira, Aviv Tamar:
A Deep Reinforcement Learning Perspective on Internet Congestion Control. 3050-3059 - Dasaem Jeong, Taegyun Kwon, Yoojin Kim, Juhan Nam:
Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance. 3060-3070 - Taewon Jeong, Youngmin Lee, Heeyoung Kim:
Ladder Capsule Network. 3071-3079 - Jongheon Jeong, Jinwoo Shin:
Training CNNs with Selective Allocation of Channels. 3080-3090 - Yeonwoo Jeong, Hyun Oh Song:
Learning Discrete and Continuous Factors of Data via Alternating Disentanglement. 3091-3099 - Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang:
Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization. 3100-3109 - Zhengyao Jiang, Shan Luo:
Neural Logic Reinforcement Learning. 3110-3119 - Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris:
Finding Options that Minimize Planning Time. 3120-3129 - Yuu Jinnai, Jee Won Park, David Abel, George Dimitri Konidaris:
Discovering Options for Exploration by Minimizing Cover Time. 3130-3139 - Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays, Bernhard Schölkopf:
Kernel Mean Matching for Content Addressability of GANs. 3140-3151 - David John, Vincent Heuveline, Michael Schober:
GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver. 3152-3162 - Kwang-Sung Jun, Rebecca Willett, Stephen J. Wright, Robert D. Nowak:
Bilinear Bandits with Low-rank Structure. 3163-3172 - Anson Kahng, Min Kyung Lee, Ritesh Noothigattu, Ariel D. Procaccia, Christos-Alexandros Psomas:
Statistical Foundations of Virtual Democracy. 3173-3182 - Hiroshi Kajino:
Molecular Hypergraph Grammar with Its Application to Molecular Optimization. 3183-3191 - Dimitris Kalimeris, Gal Kaplun, Yaron Singer:
Robust Influence Maximization for Hyperparametric Models. 3192-3200 - Nathan Kallus:
Classifying Treatment Responders Under Causal Effect Monotonicity. 3201-3210 - Ashwin Kalyan, Peter Anderson, Stefan Lee, Dhruv Batra:
Trainable Decoding of Sets of Sequences for Neural Sequence Models. 3211-3221 - Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider, Barnabás Póczos:
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments. 3222-3232 - Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer:
Differentially Private Learning of Geometric Concepts. 3233-3241 - Christos Kaplanis, Murray Shanahan, Claudia Clopath:
Policy Consolidation for Continual Reinforcement Learning. 3242-3251 - Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi:
Error Feedback Fixes SignSGD and other Gradient Compression Schemes. 3252-3261 - Hiroyuki Kasai, Pratik Jawanpuria, Bamdev Mishra:
Riemannian adaptive stochastic gradient algorithms on matrix manifolds. 3262-3271 - Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura, Petr Kellnhofer, Wojciech Matusik:
Neural Inverse Knitting: From Images to Manufacturing Instructions. 3272-3281 - Angelos Katharopoulos, François Fleuret:
Processing Megapixel Images with Deep Attention-Sampling Models. 3282-3291 - Ashish Katiyar, Jessica Hoffmann, Constantine Caramanis:
Robust Estimation of Tree Structured Gaussian Graphical Models. 3292-3300 - Yigitcan Kaya, Sanghyun Hong, Tudor Dumitras:
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking. 3301-3310 - Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam, Silvio Lattanzi, Amin Karbasi:
Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity. 3311-3320 - Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth:
Adaptive Scale-Invariant Online Algorithms for Learning Linear Models. 3321-3330 - Tom Kenter, Vincent Wan, Chun-an Chan, Rob Clark, Jakub Vit:
CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network. 3331-3340 - Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer:
Collaborative Evolutionary Reinforcement Learning. 3341-3350 - Renata Khasanova, Pascal Frossard:
Geometry Aware Convolutional Filters for Omnidirectional Images Representation. 3351-3359 - Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song:
EMI: Exploration with Mutual Information. 3360-3369 - Sungwon Kim, Sang-gil Lee, Jongyoon Song, Jaehyeon Kim, Sungroh Yoon:
FloWaveNet : A Generative Flow for Raw Audio. 3370-3378 - Youngjin Kim, Wontae Nam, Hyunwoo Kim, Ji-Hoon Kim, Gunhee Kim:
Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty. 3379-3388 - Gi-Soo Kim, Myunghee Cho Paik:
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model. 3389-3397 - Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo, Larry A. Wasserman:
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension. 3398-3407 - Friso H. Kingma, Pieter Abbeel, Jonathan Ho:
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables. 3408-3417 - Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia:
CompILE: Compositional Imitation Learning and Execution. 3418-3428 - Johannes Kirschner, Mojmir Mutny, Nicole Hiller, Rasmus Ischebeck, Andreas Krause:
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces. 3429-3438 - Ross Kleiman, David Page:
AUCμ: A Performance Metric for Multi-Class Machine Learning Models. 3439-3447 - Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern:
Fair k-Center Clustering for Data Summarization. 3448-3457 - Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern:
Guarantees for Spectral Clustering with Fairness Constraints. 3458-3467 - Ching-Yun Ko, Zhaoyang Lyu, Lily Weng, Luca Daniel, Ngai Wong, Dahua Lin:
POPQORN: Quantifying Robustness of Recurrent Neural Networks. 3468-3477 - Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi:
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication. 3478-3487 - Nikola Konstantinov, Christoph Lampert:
Robust Learning from Untrusted Sources. 3488-3498 - Wouter Kool, Herke van Hoof, Max Welling:
Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. 3499-3508 - Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia:
LIT: Learned Intermediate Representation Training for Model Compression. 3509-3518 - Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey E. Hinton:
Similarity of Neural Network Representations Revisited. 3519-3529 - Alexey Kroshnin, Nazarii Tupitsa, Darina Dvinskikh, Pavel E. Dvurechensky, Alexander V. Gasnikov, Cesar A. Uribe:
On the Complexity of Approximating Wasserstein Barycenters. 3530-3540 - Andrei Kulunchakov, Julien Mairal:
Estimate Sequences for Variance-Reduced Stochastic Composite Optimization. 3541-3550 - Ravi Kumar, Rina Panigrahy, Ali Rahimi, David P. Woodruff:
Faster Algorithms for Binary Matrix Factorization. 3551-3559 - Daniel Kunin, Jonathan M. Bloom, Aleksandrina Goeva, Cotton Seed:
Loss Landscapes of Regularized Linear Autoencoders. 3560-3569 - Han-Wen Kuo, Yenson Lau, Yuqian Zhang, John Wright:
Geometry and Symmetry in Short-and-Sparse Deconvolution. 3570-3580 - Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly:
A Large-Scale Study on Regularization and Normalization in GANs. 3581-3590 - Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva:
Making Decisions that Reduce Discriminatory Impacts. 3591-3600 - Branislav Kveton, Csaba Szepesvári, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh:
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. 3601-3610 - Antoine Labatie:
Characterizing Well-Behaved vs. Pathological Deep Neural Networks. 3611-3621 - Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas, Yoshua Bengio, Michael Mozer:
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations. 3622-3631 - Sylvain Lamprier:
A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion. 3632-3641 - Joong-Ho Won, Jason Xu, Kenneth Lange:
Projection onto Minkowski Sums with Application to Constrained Learning. 3642-3651 - Romain Laroche, Paul Trichelair, Remi Tachet des Combes:
Safe Policy Improvement with Baseline Bootstrapping. 3652-3661 - Silvio Lattanzi, Christian Sohler:
A Better k-means++ Algorithm via Local Search. 3662-3671 - Marc Teva Law, Renjie Liao, Jake Snell, Richard S. Zemel:
Lorentzian Distance Learning for Hyperbolic Representations. 3672-3681 - Andrew R. Lawrence, Carl Henrik Ek, Neill D. F. Campbell:
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures. 3682-3691 - Yasin Abbasi-Yadkori, Peter L. Bartlett, Kush Bhatia, Nevena Lazic, Csaba Szepesvári, Gellért Weisz:
POLITEX: Regret Bounds for Policy Iteration using Expert Prediction. 3692-3702 - Hoang Minh Le, Cameron Voloshin, Yisong Yue:
Batch Policy Learning under Constraints. 3703-3712 - Donghwan Lee, Niao He:
Target-Based Temporal-Difference Learning. 3713-3722 - Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi S. Jaakkola:
Functional Transparency for Structured Data: a Game-Theoretic Approach. 3723-3733 - Junhyun Lee, Inyeop Lee, Jaewoo Kang:
Self-Attention Graph Pooling. 3734-3743 - Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh:
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks. 3744-3753 - Ching-pei Lee, Stephen J. Wright:
First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems. 3754-3762 - Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, Jinwoo Shin:
Robust Inference via Generative Classifiers for Handling Noisy Labels. 3763-3772 - Yifan Lei, Qiang Huang, Mohan S. Kankanhalli, Anthony K. H. Tung:
Sublinear Time Nearest Neighbor Search over Generalized Weighted Space. 3773-3781 - Matthieu Lerasle, Zoltán Szabó, Timothée Mathieu, Guillaume Lecué:
MONK Outlier-Robust Mean Embedding Estimation by Median-of-Means. 3782-3793 - Mario Lezcano Casado, David Martínez-Rubio:
Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group. 3794-3803 - Yingzhen Li, John Bradshaw, Yash Sharma:
Are Generative Classifiers More Robust to Adversarial Attacks? 3804-3814 - Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu:
Sublinear quantum algorithms for training linear and kernel-based classifiers. 3815-3824 - Huai-Yu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu:
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning. 3825-3834 - Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli:
Graph Matching Networks for Learning the Similarity of Graph Structured Objects. 3835-3845 - Yang Li, Lukasz Kaiser, Samy Bengio, Si Si:
Area Attention. 3846-3855 - Shuai Li, Tor Lattimore, Csaba Szepesvári:
Online Learning to Rank with Features. 3856-3865 - Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong:
NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks. 3866-3876 - Zehang Richard Li, Tyler H. McCormick, Samuel J. Clark:
Bayesian Joint Spike-and-Slab Graphical Lasso. 3877-3885 - Yuan Li, Benjamin I. P. Rubinstein, Trevor Cohn:
Exploiting Worker Correlation for Label Aggregation in Crowdsourcing. 3886-3895 - Juncheng Li, Frank R. Schmidt, J. Zico Kolter:
Adversarial camera stickers: A physical camera-based attack on deep learning systems. 3896-3904 - Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic:
Towards a Unified Analysis of Random Fourier Features. 3905-3914 - Yiying Li, Yongxin Yang, Wei Zhou, Timothy M. Hospedales:
Feature-Critic Networks for Heterogeneous Domain Generalization. 3915-3924 - Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, Caiming Xiong:
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting. 3925-3934 - Qiuwei Li, Zhihui Zhu, Gongguo Tang:
Alternating Minimizations Converge to Second-Order Optimal Solutions. 3935-3943 - Nikolaos Liakopoulos, Apostolos Destounis, Georgios S. Paschos, Thrasyvoulos Spyropoulos, Panayotis Mertikopoulos:
Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints. 3944-3952 - Jan Malte Lichtenberg, Özgür Simsek:
Regularization in directable environments with application to Tetris. 3953-3962 - Valerii Likhosherstov, Yury Maximov, Misha Chertkov:
Inference and Sampling of $K_33$-free Ising Models. 3963-3972 - Shiau Hong Lim, Arnaud Autef:
Kernel-Based Reinforcement Learning in Robust Markov Decision Processes. 3973-3981 - Tianyi Lin, Nhat Ho, Michael I. Jordan:
On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms. 3982-3991 - Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt:
Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations. 3992-4002 - Yanli Liu, Fei Feng, Wotao Yin:
Acceleration of SVRG and Katyusha X by Inexact Preconditioning. 4003-4012 - Hong Liu, Mingsheng Long, Jianmin Wang, Michael I. Jordan:
Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers. 4013-4022 - Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon D. McAuliffe:
Rao-Blackwellized Stochastic Gradients for Discrete Distributions. 4023-4031 - Weiwei Liu, Xiaobo Shen:
Sparse Extreme Multi-label Learning with Oracle Property. 4032-4041 - Fang Liu, Ness B. Shroff:
Data Poisoning Attacks on Stochastic Bandits. 4042-4050 - Lydia T. Liu, Max Simchowitz, Moritz Hardt:
The Implicit Fairness Criterion of Unconstrained Learning. 4051-4060 - Hao Liu, Richard Socher, Caiming Xiong:
Taming MAML: Efficient unbiased meta-reinforcement learning. 4061-4071 - Chen Liu, Ryota Tomioka, Volkan Cevher:
On Certifying Non-Uniform Bounds against Adversarial Attacks. 4072-4081 - Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu:
Understanding and Accelerating Particle-Based Variational Inference. 4082-4092 - Chang Liu, Jingwei Zhuo, Jun Zhu:
Understanding MCMC Dynamics as Flows on the Wasserstein Space. 4093-4103 - Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, Fabian-Robert Stöter:
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions. 4104-4113 - Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem:
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. 4114-4124 - Ben London, Ted Sandler:
Bayesian Counterfactual Risk Minimization. 4125-4133 - Songtao Lu, Mingyi Hong, Zhengdao Wang:
PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization. 4134-4143 - Sidi Lu, Jiayuan Mao, Joshua B. Tenenbaum, Jiajun Wu:
Neurally-Guided Structure Inference. 4144-4153 - Shiyin Lu, Guanghui Wang, Yao Hu, Lijun Zhang:
Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards. 4154-4163 - Sidi Lu, Lantao Yu, Siyuan Feng, Yaoming Zhu, Weinan Zhang:
CoT: Cooperative Training for Generative Modeling of Discrete Data. 4164-4172 - Carlo Lucibello, Luca Saglietti, Yue M. Lu:
Generalized Approximate Survey Propagation for High-Dimensional Estimation. 4173-4182 - Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly:
High-Fidelity Image Generation With Fewer Labels. 4183-4192 - Giulia Luise, Dimitrios Stamos, Massimiliano Pontil, Carlo Ciliberto:
Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction. 4193-4202 - Ping Luo, Zhanglin Peng, Wenqi Shao, Ruimao Zhang, Jiamin Ren, Lingyun Wu:
Differentiable Dynamic Normalization for Learning Deep Representation. 4203-4211 - Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu:
Disentangled Graph Convolutional Networks. 4212-4221 - Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato:
Variational Implicit Processes. 4222-4233 - Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin, Cheng Zhang:
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE. 4234-4243 - Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari:
Bayesian leave-one-out cross-validation for large data. 4244-4253 - Sepideh Mahabadi, Piotr Indyk, Shayan Oveis Gharan, Alireza Rezaei:
Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm. 4254-4263 - Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-Dickstein:
Guided evolutionary strategies: augmenting random search with surrogate gradients. 4264-4273 - Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed:
Data Poisoning Attacks in Multi-Party Learning. 4274-4283 - Michael W. Mahoney, Charles H. Martin:
Traditional and Heavy Tailed Self Regularization in Neural Network Models. 4284-4293 - Vien V. Mai, Mikael Johansson:
Curvature-Exploiting Acceleration of Elastic Net Computations. 4294-4303 - Ashok Vardhan Makkuva, Pramod Viswanath, Sreeram Kannan, Sewoong Oh:
Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms. 4304-4313 - Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour, Stefano Ermon:
Calibrated Model-Based Deep Reinforcement Learning. 4314-4323 - Timothy A. Mann, Sven Gowal, András György, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan:
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems. 4324-4332 - Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová:
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models. 4333-4342 - Jingkai Mao, Jakob N. Foerster, Tim Rocktäschel, Maruan Al-Shedivat, Gregory Farquhar, Shimon Whiteson:
A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs. 4343-4351 - Andrés Marafioti, Nathanaël Perraudin, Nicki Holighaus, Piotr Majdak:
Adversarial Generation of Time-Frequency Features with application in audio synthesis. 4352-4362 - Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman:
On the Universality of Invariant Networks. 4363-4371 - Kaspar Märtens, Kieran R. Campbell, Christopher Yau:
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models. 4372-4381 - Jérémie Mary, Clément Calauzènes, Noureddine El Karoui:
Fairness-Aware Learning for Continuous Attributes and Treatments. 4382-4391 - Alexander Mathiasen, Kasper Green Larsen, Allan Grønlund:
Optimal Minimal Margin Maximization with Boosting. 4392-4401 - Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh:
Disentangling Disentanglement in Variational Autoencoders. 4402-4412 - Pierre-Alexandre Mattei, Jes Frellsen:
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets. 4413-4423 - Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu:
Distributional Reinforcement Learning for Efficient Exploration. 4424-4434 - Ryan McKenna, Daniel Sheldon, Gerome Miklau:
Graphical-model based estimation and inference for differential privacy. 4435-4444 - Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds:
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems. 4445-4455 - L. Elisa Celis, Anay Mehrotra, Nisheeth K. Vishnoi:
Toward Controlling Discrimination in Online Ad Auctions. 4456-4465 - Nikhil Mehta, Lawrence Carin, Piyush Rai:
Stochastic Blockmodels meet Graph Neural Networks. 4466-4474 - Hongyuan Mei, Guanghui Qin, Jason Eisner:
Imputing Missing Events in Continuous-Time Event Streams. 4475-4485 - Eldad Meller, Alexander Finkelstein, Uri Almog, Mark Grobman:
Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization. 4486-4495 - Facundo Mémoli, Zane T. Smith, Zhengchao Wan:
The Wasserstein Transform. 4496-4504 - Charith Mendis, Alex Renda, Saman P. Amarasinghe, Michael Carbin:
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks. 4505-4515 - Arthur Mensch, Mathieu Blondel, Gabriel Peyré:
Geometric Losses for Distributional Learning. 4516-4525 - Pedro Mercado, Francesco Tudisco, Matthias Hein:
Spectral Clustering of Signed Graphs via Matrix Power Means. 4526-4536 - Michael R. Metel, Akiko Takeda:
Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization. 4537-4545 - Alberto Maria Metelli, Emanuele Ghelfi, Marcello Restelli:
Reinforcement Learning in Configurable Continuous Environments. 4546-4555 - Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein:
Understanding and correcting pathologies in the training of learned optimizers. 4556-4565 - Raphael A. Meyer, Jean Honorio:
Optimality Implies Kernel Sum Classifiers are Statistically Efficient. 4566-4574 - Poorya Mianjy, Raman Arora:
On Dropout and Nuclear Norm Regularization. 4575-4584 - Andrew C. Miller, Ziad Obermeyer, John P. Cunningham, Sendhil Mullainathan:
Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography. 4585-4594 - Ardalan Mirshani, Matthew Reimherr, Aleksandra B. Slavkovic:
Formal Privacy for Functional Data with Gaussian Perturbations. 4595-4604 - Gal Mishne, Eric C. Chi, Ronald R. Coifman:
Co-manifold learning with missing data. 4605-4614 - Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh:
Agnostic Federated Learning. 4615-4625 - Thomas Möllenhoff, Daniel Cremers:
Flat Metric Minimization with Applications in Generative Modeling. 4626-4635 - Seungyong Moon, Gaon An, Hyun Oh Song:
Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization. 4636-4645 - Hesham Mostafa, Xin Wang:
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization. 4646-4655 - Michael Muehlebach, Michael I. Jordan:
A Dynamical Systems Perspective on Nesterov Acceleration. 4656-4662 - Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak A. Rao, Bruno Ribeiro:
Relational Pooling for Graph Representations. 4663-4673 - Razieh Nabi, Daniel Malinsky, Ilya Shpitser:
Learning Optimal Fair Policies. 4674-4682 - Mor Shpigel Nacson, Suriya Gunasekar, Jason D. Lee, Nathan Srebro, Daniel Soudry:
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models. 4683-4692 - Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama:
A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning. 4693-4702 - Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli:
SGD without Replacement: Sharper Rates for General Smooth Convex Functions. 4703-4711 - Eric T. Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth:
Dropout as a Structured Shrinkage Prior. 4712-4722 - Eric T. Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Görür, Balaji Lakshminarayanan:
Hybrid Models with Deep and Invertible Features. 4723-4732 - Jinseok Nam, Young-Bum Kim, Eneldo Loza Mencía, Sunghyun Park, Ruhi Sarikaya, Johannes Fürnkranz:
Learning Context-dependent Label Permutations for Multi-label Classification. 4733-4742 - Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, Anirban Chakraborty:
Zero-Shot Knowledge Distillation in Deep Networks. 4743-4751 - Amin Nayebi, Alexander Munteanu, Matthias Poloczek:
A Framework for Bayesian Optimization in Embedded Subspaces. 4752-4761 - Seyedehsara Nayer, Praneeth Narayanamurthy, Namrata Vaswani:
Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements. 4762-4770 - Eugène Ndiaye, Tam Le, Olivier Fercoq, Joseph Salmon, Ichiro Takeuchi:
Safe Grid Search with Optimal Complexity. 4771-4780 - Thomas Nedelec, Noureddine El Karoui, Vianney Perchet:
Learning to bid in revenue-maximizing auctions. 4781-4789 - Quynh Nguyen:
On Connected Sublevel Sets in Deep Learning. 4790-4799 - Duc Tam Nguyen, Zhongyu Lou, Michael Klar, Thomas Brox:
Anomaly Detection With Multiple-Hypotheses Predictions. 4800-4809 - Thanh Huy Nguyen, Umut Simsekli, Gaël Richard:
Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization. 4810-4819 - Rajbir-Singh Nirwan, Nils Bertschinger:
Rotation Invariant Householder Parameterization for Bayesian PCA. 4820-4828 - Richard Nock, Robert C. Williamson:
Lossless or Quantized Boosting with Integer Arithmetic. 4829-4838 - Arild Nøkland, Lars Hiller Eidnes:
Training Neural Networks with Local Error Signals. 4839-4850 - Guido Novati, Petros Koumoutsakos:
Remember and Forget for Experience Replay. 4851-4860 - Maxwell I. Nye, Luke B. Hewitt, Joshua B. Tenenbaum, Armando Solar-Lezama:
Learning to Infer Program Sketches. 4861-4870 - Fritz Obermeyer, Eli Bingham, Martin Jankowiak, Neeraj Pradhan, Justin T. Chiu, Alexander M. Rush, Noah D. Goodman:
Tensor Variable Elimination for Plated Factor Graphs. 4871-4880 - Michael Oberst, David A. Sontag:
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models. 4881-4890 - Peter Ochs, Yura Malitsky:
Model Function Based Conditional Gradient Method with Armijo-like Line Search. 4891-4900 - Augustus Odena, Catherine Olsson, David G. Andersen, Ian J. Goodfellow:
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing. 4901-4911 - Dino Oglic, Thomas Gärtner:
Scalable Learning in Reproducing Kernel Krein Spaces. 4912-4921 - Kenta Oono, Taiji Suzuki:
Approximation and non-parametric estimation of ResNet-type convolutional neural networks. 4922-4931 - Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu:
Orthogonal Random Forest for Causal Inference. 4932-4941 - Muhammad Osama, Dave Zachariah, Thomas B. Schön:
Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding. 4942-4950 - Samet Oymak, Mahdi Soltanolkotabi:
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? 4951-4960 - Ioannis Panageas, Georgios Piliouras, Xiao Wang:
Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always. 4961-4969 - Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu:
Improving Adversarial Robustness via Promoting Ensemble Diversity. 4970-4979 - Konstantinos P. Panousis, Sotirios Chatzis, Sergios Theodoridis:
Nonparametric Bayesian Deep Networks with Local Competition. 4980-4988 - Matteo Papini, Alberto Maria Metelli, Lorenzo Lupo, Marcello Restelli:
Optimistic Policy Optimization via Multiple Importance Sampling. 4989-4999 - Nikolaos Pappas, James Henderson:
Deep Residual Output Layers for Neural Language Generation. 5000-5011 - Vardan Papyan:
Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians. 5012-5021 - Sobhan Naderi Parizi, Kun He, Reza Aghajani, Stan Sclaroff, Pedro F. Felzenszwalb:
Generalized Majorization-Minimization. 5022-5031 - Yookoon S. Park, Chris Dongjoo Kim, Gunhee Kim:
Variational Laplace Autoencoders. 5032-5041 - Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith:
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study. 5042-5051 - Sejun Park, Eunho Yang, Se-Young Yun, Jinwoo Shin:
Spectral Approximate Inference. 5052-5061 - Deepak Pathak, Dhiraj Gandhi, Abhinav Gupta:
Self-Supervised Exploration via Disagreement. 5062-5071 - François-Pierre Paty, Marco Cuturi:
Subspace Robust Wasserstein Distances. 5072-5081 - Supratik Paul, Michael A. Osborne, Shimon Whiteson:
Fingerprint Policy Optimisation for Robust Reinforcement Learning. 5082-5091 - Xi Peng, Zhenyu Huang, Jiancheng Lv, Hongyuan Zhu, Joey Tianyi Zhou:
COMIC: Multi-view Clustering Without Parameter Selection. 5092-5101 - Xingchao Peng, Zijun Huang, Ximeng Sun, Kate Saenko:
Domain Agnostic Learning with Disentangled Representations. 5102-5112 - Hanyu Peng, Jiaxiang Wu, Shifeng Chen, Junzhou Huang:
Collaborative Channel Pruning for Deep Networks. 5113-5122 - Pierre Perrault, Vianney Perchet, Michal Valko:
Exploiting structure of uncertainty for efficient matroid semi-bandits. 5123-5132 - David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell, Thomas L. Griffiths:
Cognitive model priors for predicting human decisions. 5133-5141 - Mary Phuong, Christoph Lampert:
Towards Understanding Knowledge Distillation. 5142-5151 - A. J. Piergiovanni, Michael S. Ryoo:
Temporal Gaussian Mixture Layer for Videos. 5152-5161 - Vladislav Polianskii, Florian T. Pokorny:
Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration. 5162-5170 - Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, George Tucker:
On Variational Bounds of Mutual Information. 5171-5180 - Manish Purohit, Sreenivas Gollapudi, Manish Raghavan:
Hiring Under Uncertainty. 5181-5189 - Xun Qian, Zheng Qu, Peter Richtárik:
SAGA with Arbitrary Sampling. 5190-5199 - Xun Qian, Peter Richtárik, Robert M. Gower, Alibek Sailanbayev, Nicolas Loizou, Egor Shulgin:
SGD with Arbitrary Sampling: General Analysis and Improved Rates. 5200-5209 - Kaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, Mark Hasegawa-Johnson:
AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss. 5210-5219 - Aurick Qiao, Bryon Aragam, Bingjing Zhang, Eric P. Xing:
Fault Tolerance in Iterative-Convergent Machine Learning. 5220-5230 - Yao Qin, Nicholas Carlini, Garrison W. Cottrell, Ian J. Goodfellow, Colin Raffel:
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. 5231-5240 - Meng Qu, Yoshua Bengio, Jian Tang:
GMNN: Graph Markov Neural Networks. 5241-5250 - Chao Qu, Shie Mannor, Huan Xu:
Nonlinear Distributional Gradient Temporal-Difference Learning. 5251-5260 - Goran Radanovic, Rati Devidze, David C. Parkes, Adish Singla:
Learning to Collaborate in Markov Decision Processes. 5261-5270 - Jack W. Rae, Sergey Bartunov, Timothy P. Lillicrap:
Meta-Learning Neural Bloom Filters. 5271-5280 - Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Robert D. Kleinberg, Sendhil Mullainathan, Jon M. Kleinberg:
Direct Uncertainty Prediction for Medical Second Opinions. 5281-5290 - Arvind U. Raghunathan, Anoop Cherian, Devesh K. Jha:
Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function. 5291-5300 - Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred A. Hamprecht, Yoshua Bengio, Aaron C. Courville:
On the Spectral Bias of Neural Networks. 5301-5310 - Tahrima Rahman, Shasha Jin, Vibhav Gogate:
Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation. 5311-5320 - Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris S. Papailiopoulos:
Does Data Augmentation Lead to Positive Margin? 5321-5330 - Kate Rakelly, Aurick Zhou, Chelsea Finn, Sergey Levine, Deirdre Quillen:
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. 5331-5340 - Alain Rakotomamonjy, Gilles Gasso, Joseph Salmon:
Screening rules for Lasso with non-convex Sparse Regularizers. 5341-5350 - Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Krishnan Mody:
Topological Data Analysis of Decision Boundaries with Application to Model Selection. 5351-5360 - Neale Ratzlaff, Fuxin Li:
HyperGAN: A Generative Model for Diverse, Performant Neural Networks. 5361-5369 - Sujith Ravi:
Efficient On-Device Models using Neural Projections. 5370-5379 - Ramin Raziperchikolaei, Harish S. Bhat:
A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation. 5380-5388 - Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar:
Do ImageNet Classifiers Generalize to ImageNet? 5389-5400 - Henry W. J. Reeve, Ata Kabán:
Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise. 5401-5409 - Yi Ren, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu:
Almost Unsupervised Text to Speech and Automatic Speech Recognition. 5410-5419 - Hongyu Ren, Shengjia Zhao, Stefano Ermon:
Adaptive Antithetic Sampling for Variance Reduction. 5420-5428 - Alon Resler, Yishay Mansour:
Adversarial Online Learning with noise. 5429-5437 - Alireza Rezaei, Shayan Oveis Gharan:
A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes. 5438-5447 - Bastian Rieck, Christian Bock, Karsten M. Borgwardt:
A Persistent Weisfeiler-Lehman Procedure for Graph Classification. 5448-5458 - Paul Rolland, Ali Kavis, Alexander Immer, Adish Singla, Volkan Cevher:
Efficient learning of smooth probability functions from Bernoulli tests with guarantees. 5459-5467 - Joshua Romoff, Peter Henderson, Ahmed Touati, Yann Ollivier, Joelle Pineau, Emma Brunskill:
Separable value functions across time-scales. 5468-5477 - Aviv Rosenberg, Yishay Mansour:
Online Convex Optimization in Adversarial Markov Decision Processes. 5478-5486 - Simone Rossi, Pietro Michiardi, Maurizio Filippone:
Good Initializations of Variational Bayes for Deep Models. 5487-5497 - Kevin Roth, Yannic Kilcher, Thomas Hofmann:
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples. 5498-5507 - Grant M. Rotskoff, Samy Jelassi, Joan Bruna, Eric Vanden-Eijnden:
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically. 5508-5517 - Vincent Roulet, Dmitriy Drusvyatskiy, Siddhartha S. Srinivasa, Zaïd Harchaoui:
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees. 5518-5527 - Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney:
Statistics and Samples in Distributional Reinforcement Learning. 5528-5536 - Francisco J. R. Ruiz, Michalis K. Titsias:
A Contrastive Divergence for Combining Variational Inference and MCMC. 5537-5545 - Ernest K. Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin:
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers. 5546-5557 - Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, Hervé Jégou:
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference. 5558-5567 - Sam Safavi, José Bento:
Tractable n-Metrics for Multiple Graphs. 5568-5578 - Touqir Sajed, Or Sheffet:
An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule. 5579-5588 - Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth:
Deep Gaussian Processes with Importance-Weighted Variational Inference. 5589-5598 - Richard Santiago, F. Bruce Shepherd:
Multivariate Submodular Optimization. 5599-5609 - Tuhin Sarkar, Alexander Rakhlin:
Near optimal finite time identification of arbitrary linear dynamical systems. 5610-5618 - Ikuro Sato, Kohta Ishikawa, Guoqing Liu, Masayuki Tanaka:
Breaking Inter-Layer Co-Adaptation by Classifier Anonymization. 5619-5627 - Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora, Mikhail Khodak, Hrishikesh Khandeparkar:
A Theoretical Analysis of Contrastive Unsupervised Representation Learning. 5628-5637 - Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield, Mingyuan Zhou, Hanna M. Wallach:
Locally Private Bayesian Inference for Count Models. 5638-5648 - Julien Schroeter, Kirill A. Sidorov, A. David Marshall:
Weakly-Supervised Temporal Localization via Occurrence Count Learning. 5649-5659 - Arjun Seshadri, Alex Peysakhovich, Johan Ugander:
Discovering Context Effects from Raw Choice Data. 5660-5669 - Rohin Shah, Noah Gundotra, Pieter Abbeel, Anca D. Dragan:
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference. 5670-5679 - Lior Shani, Yonathan Efroni, Shie Mannor:
Exploration Conscious Reinforcement Learning Revisited. 5680-5689 - Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant:
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data. 5690-5700 - Yujia Shen, Haiying Huang, Arthur Choi, Adnan Darwiche:
Conditional Independence in Testing Bayesian Networks. 5701-5709 - Weiran Shen, Sébastien Lahaie, Renato Paes Leme:
Learning to Clear the Market. 5710-5718 - Tianxiao Shen, Myle Ott, Michael Auli, Marc'Aurelio Ranzato:
Mixture Models for Diverse Machine Translation: Tricks of the Trade. 5719-5728 - Zebang Shen, Alejandro Ribeiro, Hamed Hassani, Hui Qian, Chao Mi:
Hessian Aided Policy Gradient. 5729-5738 - Yanyao Shen, Sujay Sanghavi:
Learning with Bad Training Data via Iterative Trimmed Loss Minimization. 5739-5748 - Alex Shestopaloff, Arnaud Doucet:
Replica Conditional Sequential Monte Carlo. 5749-5757 - Jiaxin Shi, Mohammad Emtiyaz Khan, Jun Zhu:
Scalable Training of Inference Networks for Gaussian-Process Models. 5758-5768 - Weishi Shi, Qi Yu:
Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning. 5769-5778 - Pranav Shyam, Wojciech Jaskowski, Faustino Gomez:
Model-Based Active Exploration. 5779-5788 - Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Alexander Levis:
Rehashing Kernel Evaluation in High Dimensions. 5789-5798 - Loïc Simon, Ryan Webster, Julien Rabin:
Revisiting precision recall definition for generative modeling. 5799-5808 - Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, David Lopez-Paz:
First-Order Adversarial Vulnerability of Neural Networks and Input Dimension. 5809-5817 - Kirill Simonov, Fedor V. Fomin, Petr A. Golovach, Fahad Panolan:
Refined Complexity of PCA with Outliers. 5818-5826 - Umut Simsekli, Levent Sagun, Mert Gürbüzbalaban:
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks. 5827-5837 - Rajhans Singh, Pavan K. Turaga, Suren Jayasuriya, Ravi Garg, Martin W. Braun:
Non-Parametric Priors For Generative Adversarial Networks. 5838-5847 - Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi:
Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation. 5848-5856 - Lotfi Slim, Clément Chatelain, Chloé-Agathe Azencott, Jean-Philippe Vert:
kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection. 5857-5865 - Edward J. Smith, Scott Fujimoto, Adriana Romero, David Meger:
GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects. 5866-5876 - David R. So, Quoc V. Le, Chen Liang:
The Evolved Transformer. 5877-5886 - Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Hostallero, Yung Yi:
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning. 5887-5896 - Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach:
Distribution calibration for regression. 5897-5906 - Hwanjun Song, Minseok Kim, Jae-Gil Lee:
SELFIE: Refurbishing Unclean Samples for Robust Deep Learning. 5907-5915 - Zhao Song, Ronald Parr, Lawrence Carin:
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective. 5916-5925 - Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu:
MASS: Masked Sequence to Sequence Pre-training for Language Generation. 5926-5936 - Pedro Soto, Jun Li, Xiaodi Fan:
Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication. 5937-5945 - Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava:
Compressing Gradient Optimizers via Count-Sketches. 5946-5955 - Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra:
Escaping Saddle Points with Adaptive Gradient Methods. 5956-5965 - Karl Stelzner, Robert Peharz, Kristian Kersting:
Faster Attend-Infer-Repeat with Tractable Probabilistic Models. 5966-5975 - Mitchell Stern, William Chan, Jamie Kiros, Jakob Uszkoreit:
Insertion Transformer: Flexible Sequence Generation via Insertion Operations. 5976-5985 - Asa Cooper Stickland, Iain Murray:
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning. 5986-5995 - Matthew Streeter:
Learning Optimal Linear Regularizers. 5996-6004 - Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims:
CAB: Continuous Adaptive Blending for Policy Evaluation and Learning. 6005-6014 - Bing Su, Ying Wu:
Learning Distance for Sequences by Learning a Ground Metric. 6015-6025 - Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro:
Contextual Memory Trees. 6026-6035 - Wen Sun, Anirudh Vemula, Byron Boots, Drew Bagnell:
Provably Efficient Imitation Learning from Observation Alone. 6036-6045 - Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, Samuel Kaski:
Active Learning for Decision-Making from Imbalanced Observational Data. 6046-6055 - Raphael Suter, Ðorðe Miladinovic, Bernhard Schölkopf, Stefan Bauer:
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness. 6056-6065 - Ryota Suzuki, Ryusuke Takahama, Shun Onoda:
Hyperbolic Disk Embeddings for Directed Acyclic Graphs. 6066-6075 - Amirhossein Taghvaei, Prashant G. Mehta:
Accelerated Flow for Probability Distributions. 6076-6085 - Kai Sheng Tai, Peter Bailis, Gregory Valiant:
Equivariant Transformer Networks. 6086-6095 - Corentin Tallec, Léonard Blier, Yann Ollivier:
Making Deep Q-learning methods robust to time discretization. 6096-6104 - Mingxing Tan, Quoc V. Le:
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 6105-6114 - Ping Liang Tan, Robert Peharz:
Hierarchical Decompositional Mixtures of Variational Autoencoders. 6115-6124 - Wenpin Tang:
Mallows ranking models: maximum likelihood estimate and regeneration. 6125-6134 - Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi:
Correlated Variational Auto-Encoders. 6135-6144 - Da Tang, Rajesh Ranganath:
The Variational Predictive Natural Gradient. 6145-6154 - Hanlin Tang, Chen Yu, Xiangru Lian, Tong Zhang, Ji Liu:
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression. 6155-6165 - Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya V. Nori:
Adaptive Neural Trees. 6166-6175 - Chenyang Tao, Shuyang Dai, Liqun Chen, Ke Bai, Junya Chen, Chang Liu, Ruiyi Zhang, Georgiy V. Bobashev, Lawrence Carin:
Variational Annealing of GANs: A Langevin Perspective. 6176-6185 - Zenna Tavares, Javier Burroni, Edgar Minasyan, Armando Solar-Lezama, Rajesh Ranganath:
Predicate Exchange: Inference with Declarative Knowledge. 6186-6195 - Guy Tennenholtz, Shie Mannor:
The Natural Language of Actions. 6196-6205 - Yoshikazu Terada, Michio Yamamoto:
Kernel Normalized Cut: a Theoretical Revisit. 6206-6214 - Chen Tessler, Yonathan Efroni, Shie Mannor:
Action Robust Reinforcement Learning and Applications in Continuous Control. 6215-6224 - Philip S. Thomas, Erik G. Learned-Miller:
Concentration Inequalities for Conditional Value at Risk. 6225-6233 - Sunil Thulasidasan, Tanmoy Bhattacharya, Jeff A. Bilmes, Gopinath Chennupati, Jamal Mohd-Yusof:
Combating Label Noise in Deep Learning using Abstention. 6234-6243 - Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick:
ELF OpenGo: an analysis and open reimplementation of AlphaZero. 6244-6253 - Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac:
Random Matrix Improved Covariance Estimation for a Large Class of Metrics. 6254-6263 - Andrea Tirinzoni, Mattia Salvini, Marcello Restelli:
Transfer of Samples in Policy Search via Multiple Importance Sampling. 6264-6274 - Titouan Vayer, Nicolas Courty, Romain Tavenard, Laetitia Chapel, Rémi Flamary:
Optimal Transport for structured data with application on graphs. 6275-6284 - Anh Tong, Jaesik Choi:
Discovering Latent Covariance Structures for Multiple Time Series. 6285-6294 - Toan Tran, Thanh-Toan Do, Ian D. Reid, Gustavo Carneiro:
Bayesian Generative Active Deep Learning. 6295-6304 - Ngoc B. Tran, Daniel R. Kepple, Sergey Shuvaev, Alexei A. Koulakov:
DeepNose: Using artificial neural networks to represent the space of odorants. 6305-6314 - Brian L. Trippe, Jonathan H. Huggins, Raj Agrawal, Tamara Broderick:
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations. 6315-6324 - William Trouleau, Jalal Etesami, Matthias Grossglauser, Negar Kiyavash, Patrick Thiran:
Learning Hawkes Processes Under Synchronization Noise. 6325-6334 - Manolis C. Tsakiris, Liangzu Peng:
Homomorphic Sensing. 6335-6344 - Ryan D. Turner, Jane Hung, Eric Frank, Yunus Saatchi, Jason Yosinski:
Metropolis-Hastings Generative Adversarial Networks. 6345-6353 - Ruo-Chun Tzeng, Shan-Hung Wu:
Distributed, Egocentric Representations of Graphs for Detecting Critical Structures. 6354-6362 - Jalaj Upadhyay:
Sublinear Space Private Algorithms Under the Sliding Window Model. 6363-6372 - Berk Ustun, Yang Liu, David C. Parkes:
Fairness without Harm: Decoupled Classifiers with Preference Guarantees. 6373-6382 - Viivi Uurtio, Sahely Bhadra, Juho Rousu:
Large-Scale Sparse Kernel Canonical Correlation Analysis. 6383-6391 - Marten van Dijk, Lam M. Nguyen, Phuong Ha Nguyen, Dzung T. Phan:
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD. 6392-6400 - Benjamin van Niekerk, Steven D. James, Adam Christopher Earle, Benjamin Rosman:
Composing Value Functions in Reinforcement Learning. 6401-6409 - Francisco Vargas, Kamen Brestnichki, Nils Hammerla:
Model Comparison for Semantic Grouping. 6410-6417 - Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, Christopher Ré:
Learning Dependency Structures for Weak Supervision Models. 6418-6427 - Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, Devi Parikh:
Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering. 6428-6437 - Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio:
Manifold Mixup: Better Representations by Interpolating Hidden States. 6438-6447 - Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham M. Kakade:
Maximum Likelihood Estimation for Learning Populations of Parameters. 6448-6457 - Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo, Julyan Arbel:
Understanding Priors in Bayesian Neural Networks at the Unit Level. 6458-6467 - Nikos Vlassis, Aurélien Bibaut, Maria Dimakopoulou, Tony Jebara:
On the Design of Estimators for Bandit Off-Policy Evaluation. 6468-6476 - Aleksandr Vorobev, Aleksei Ustimenko, Gleb Gusev, Pavel Serdyukov:
Learning to select for a predefined ranking. 6477-6486 - Edward Wagstaff, Fabian Fuchs, Martin Engelcke, Ingmar Posner, Michael A. Osborne:
On the Limitations of Representing Functions on Sets. 6487-6494 - Ian Walker, Ben Glocker:
Graph Convolutional Gaussian Processes. 6495-6504 - Tong Wang:
Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute. 6505-6514 - Chaojie Wang, Bo Chen, Sucheng Xiao, Mingyuan Zhou:
Convolutional Poisson Gamma Belief Network. 6515-6525 - Di Wang, Changyou Chen, Jinhui Xu:
Differentially Private Empirical Risk Minimization with Non-convex Loss Functions. 6526-6535 - Ruohan Wang, Carlo Ciliberto, Pierluigi Vito Amadori, Yiannis Demiris:
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation. 6536-6544 - Po-Wei Wang, Priya L. Donti, Bryan Wilder, J. Zico Kolter:
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver. 6545-6554 - Dilin Wang, ChengYue Gong, Qiang Liu:
Improving Neural Language Modeling via Adversarial Training. 6555-6565 - Chaoqi Wang, Roger B. Grosse, Sanja Fidler, Guodong Zhang:
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis. 6566-6575 - Dilin Wang, Qiang Liu:
Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models. 6576-6585 - Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu:
On the Convergence and Robustness of Adversarial Training. 6586-6595 - Cheng Wang, Mathias Niepert:
State-Regularized Recurrent Neural Networks. 6596-6606 - Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski:
Deep Factors for Forecasting. 6607-6617 - Hao Wang, Berk Ustun, Flávio P. Calmon:
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions. 6618-6627 - Di Wang, Jinhui Xu:
On Sparse Linear Regression in the Local Differential Privacy Model. 6628-6637 - Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi:
Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random. 6638-6647 - Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher:
On the Generalization Gap in Reparameterizable Reinforcement Learning. 6648-6658 - Shengjie Wang, Tianyi Zhou, Jeff A. Bilmes:
Bias Also Matters: Bias Attribution for Deep Neural Network Explanation. 6659-6667 - Shengjie Wang, Tianyi Zhou, Jeff A. Bilmes:
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs. 6668-6676 - Rachel A. Ward, Xiaoxia Wu, Léon Bottou:
AdaGrad stepsizes: sharp convergence over nonconvex landscapes. 6677-6686 - Dennis Wei, Sanjeeb Dash, Tian Gao, Oktay Günlük:
Generalized Linear Rule Models. 6687-6696 - Xiaohan Wei, Zhuoran Yang, Zhaoran Wang:
On the statistical rate of nonlinear recovery in generative models with heavy-tailed data. 6697-6706 - Gellért Weisz, András György, Csaba Szepesvári:
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration. 6707-6715 - Sean Welleck, Kianté Brantley, Hal Daumé III, Kyunghyun Cho:
Non-Monotonic Sequential Text Generation. 6716-6726 - Lily Weng, Pin-Yu Chen, Lam M. Nguyen, Mark S. Squillante, Akhilan Boopathy, Ivan V. Oseledets, Luca Daniel:
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach. 6727-6736 - Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton:
Learning deep kernels for exponential family densities. 6737-6746 - Max Westphal, Werner Brannath:
Improving Model Selection by Employing the Test Data. 6747-6756 - Jacob Whitehill, Anand Ramakrishnan:
Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth. 6757-6765 - Christian Wildner, Heinz Koeppl:
Moment-Based Variational Inference for Markov Jump Processes. 6766-6775 - William J. Wilkinson, Michael Riis Andersen, Joshua D. Reiss, Dan Stowell, Arno Solin:
End-to-End Probabilistic Inference for Nonstationary Audio Analysis. 6776-6785 - Robert C. Williamson, Aditya Krishna Menon:
Fairness risk measures. 6786-6797 - Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen:
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation. 6798-6807 - Eric Wong, Frank R. Schmidt, J. Zico Kolter:
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations. 6808-6817 - Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama:
Imitation Learning from Imperfect Demonstration. 6818-6827 - Shanshan Wu, Alex Dimakis, Sujay Sanghavi, Felix X. Yu, Daniel Niels Holtmann-Rice, Dmitry Storcheus, Afshin Rostamizadeh, Sanjiv Kumar:
Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling. 6828-6839 - Xi-Zhu Wu, Song Liu, Zhi-Hua Zhou:
Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin. 6840-6849 - Yan Wu, Mihaela Rosca, Timothy P. Lillicrap:
Deep Compressed Sensing. 6850-6860 - Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, Kilian Q. Weinberger:
Simplifying Graph Convolutional Networks. 6861-6871 - Yifan Wu, Ezra Winston, Divyansh Kaushik, Zachary C. Lipton:
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment. 6872-6881 - Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, Hongyuan Zha:
On Scalable and Efficient Computation of Large Scale Optimal Transport. 6882-6892 - Cong Xie, Sanmi Koyejo, Indranil Gupta:
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance. 6893-6901 - Xingyu Xie, Jianlong Wu, Guangcan Liu, Zhisheng Zhong, Zhouchen Lin:
Differentiable Linearized ADMM. 6902-6911 - Hanwen Xing, Geoff Nicholls, Jeong Lee:
Calibrated Approximate Bayesian Inference. 6912-6920 - Jason Xu, Kenneth Lange:
Power k-Means Clustering. 6921-6931 - Hongteng Xu, Dixin Luo, Hongyuan Zha, Lawrence Carin:
Gromov-Wasserstein Learning for Graph Matching and Node Embedding. 6932-6941 - Yi Xu, Qi Qi, Qihang Lin, Rong Jin, Tianbao Yang:
Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence. 6942-6951 - Kelvin Xu, Ellis Ratner, Anca D. Dragan, Sergey Levine, Chelsea Finn:
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning. 6952-6962 - Kai Xu, Akash Srivastava, Charles Sutton:
Variational Russian Roulette for Deep Bayesian Nonparametrics. 6963-6972 - Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew McCallum:
Supervised Hierarchical Clustering with Exponential Linkage. 6973-6983 - Kaiyu Yang, Jia Deng:
Learning to Prove Theorems via Interacting with Proof Assistants. 6984-6994 - Lin Yang, Mengdi Wang:
Sample-Optimal Parametric Q-Learning Using Linearly Additive Features. 6995-7004 - Zhaohui Yang, Yunhe Wang, Chuanjian Liu, Hanting Chen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xu:
LegoNet: Efficient Convolutional Neural Networks with Lego Filters. 7005-7014 - Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa:
SWALP : Stochastic Weight Averaging in Low Precision Training. 7015-7024 - Yuzhe Yang, Guo Zhang, Zhi Xu, Dina Katabi:
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation. 7025-7034 - Quanming Yao, James Tin-Yau Kwok, Bo Han:
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations. 7035-7044 - Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li:
Hierarchically Structured Meta-learning. 7045-7054 - Taisuke Yasuda, David P. Woodruff, Manuel Fernandez:
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering. 7055-7063 - Jong Chul Ye, Woon Kyoung Sung:
Understanding Geometry of Encoder-Decoder CNNs. 7064-7073 - Dong Yin, Yudong Chen, Kannan Ramchandran, Peter L. Bartlett:
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning. 7074-7084 - Dong Yin, Kannan Ramchandran, Peter L. Bartlett:
Rademacher Complexity for Adversarially Robust Generalization. 7085-7094 - Mingzhang Yin, Yuguang Yue, Mingyuan Zhou:
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables. 7095-7104 - Chris Ying, Aaron Klein, Eric Christiansen, Esteban Real, Kevin Murphy, Frank Hutter:
NAS-Bench-101: Towards Reproducible Neural Architecture Search. 7105-7114 - Sung Whan Yoon, Jun Seo, Jaekyun Moon:
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning. 7115-7123 - Kaichao You, Ximei Wang, Mingsheng Long, Michael I. Jordan:
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation. 7124-7133 - Jiaxuan You, Rex Ying, Jure Leskovec:
Position-aware Graph Neural Networks. 7134-7143 - Halley Young, Osbert Bastani, Mayur Naik:
Learning Neurosymbolic Generative Models via Program Synthesis. 7144-7153 - Yue Yu, Jie Chen, Tian Gao, Mo Yu:
DAG-GNN: DAG Structure Learning with Graph Neural Networks. 7154-7163 - Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W. Tsang, Masashi Sugiyama:
How does Disagreement Help Generalization against Label Corruption? 7164-7173 - Hao Yu, Rong Jin:
On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization. 7174-7183 - Hao Yu, Rong Jin, Sen Yang:
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization. 7184-7193 - Lantao Yu, Jiaming Song, Stefano Ermon:
Multi-Agent Adversarial Inverse Reinforcement Learning. 7194-7201 - Chen Yu, Hanlin Tang, Cédric Renggli, Simon Kassing, Ankit Singla, Dan Alistarh, Ce Zhang, Ji Liu:
Distributed Learning over Unreliable Networks. 7202-7212 - Jianjun Yuan, Andrew G. Lamperski:
Online Adaptive Principal Component Analysis and Its extensions. 7213-7221 - Jinyang Yuan, Bin Li, Xiangyang Xue:
Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation. 7222-7231 - Huizhuo Yuan, Yuren Zhou, Chris Junchi Li, Qingyun Sun:
Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory. 7232-7241 - Jihun Yun, Peng Zheng, Eunho Yang, Aurélie C. Lozano, Aleksandr Y. Aravkin:
Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning. 7242-7251 - Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan H. Greenewald, Trong Nghia Hoang, Yasaman Khazaeni:
Bayesian Nonparametric Federated Learning of Neural Networks. 7252-7261 - Mikhail Yurochkin, Aritra Guha, Yuekai Sun, XuanLong Nguyen:
Dirichlet Simplex Nest and Geometric Inference. 7262-7271 - Alp Yurtsever, Olivier Fercoq, Volkan Cevher:
A Conditional-Gradient-Based Augmented Lagrangian Framework. 7272-7281 - Alp Yurtsever, Suvrit Sra, Volkan Cevher:
Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator. 7282-7291 - Eloi Zablocki, Patrick Bordes, Laure Soulier, Benjamin Piwowarski, Patrick Gallinari:
Context-Aware Zero-Shot Learning for Object Recognition. 7292-7303 - Andrea Zanette, Emma Brunskill:
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds. 7304-7312 - Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao:
Global Convergence of Block Coordinate Descent in Deep Learning. 7313-7323 - Richard Zhang:
Making Convolutional Networks Shift-Invariant Again. 7324-7334 - Chicheng Zhang, Alekh Agarwal, Hal Daumé III, John Langford, Sahand Negahban:
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback. 7335-7344 - Hanrui Zhang, Yu Cheng, Vincent Conitzer:
When Samples Are Strategically Selected. 7345-7353 - Han Zhang, Ian J. Goodfellow, Dimitris N. Metaxas, Augustus Odena:
Self-Attention Generative Adversarial Networks. 7354-7363 - Guo Zhang, Hao He, Dina Katabi:
Circuit-GNN: Graph Neural Networks for Distributed Circuit Design. 7364-7373 - Songyang Zhang, Xuming He, Shipeng Yan:
LatentGNN: Learning Efficient Non-local Relations for Visual Recognition. 7374-7383 - Tong Zhang, Pan Ji, Mehrtash Harandi, Wen-bing Huang, Hongdong Li:
Neural Collaborative Subspace Clustering. 7384-7393 - Xiao Zhang, Shizhong Liao:
Incremental Randomized Sketching for Online Kernel Learning. 7394-7403 - Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan:
Bridging Theory and Algorithm for Domain Adaptation. 7404-7413 - Lijun Zhang, Tie-Yan Liu, Zhi-Hua Zhou:
Adaptive Regret of Convex and Smooth Functions. 7414-7423 - Aonan Zhang, John W. Paisley:
Random Function Priors for Correlation Modeling. 7424-7433 - Fei Zhang, Guangming Shi:
Co-Representation Network for Generalized Zero-Shot Learning. 7434-7443 - Marvin Zhang, Sharad Vikram, Laura M. Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine:
SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning. 7444-7453 - Junyu Zhang, Lin Xiao:
A Composite Randomized Incremental Gradient Method. 7454-7462 - Chenyang Zhang, Guosheng Yin:
Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models. 7463-7471 - Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan:
Theoretically Principled Trade-off between Robustness and Accuracy. 7472-7482 - Yunbo Zhang, Wenhao Yu, Greg Turk:
Learning Novel Policies For Tasks. 7483-7492 - Kai Zhang, Sheng Zhang, Jun Liu, Jun Wang, Jie Zhang:
Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization. 7493-7501 - Tianyuan Zhang, Zhanxing Zhu:
Interpreting Adversarially Trained Convolutional Neural Networks. 7502-7511 - Martin J. Zhang, James Zou, David Tse:
Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits. 7512-7522 - Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon:
On Learning Invariant Representations for Domain Adaptation. 7523-7532 - Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya R. Gupta:
Metric-Optimized Example Weights. 7533-7542 - Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang:
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting. 7543-7552 - Rui Zhao, Xudong Sun, Volker Tresp:
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning. 7553-7562 - Baojian Zhou, Feng Chen, Yiming Ying:
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization. 7563-7573 - Dongruo Zhou, Quanquan Gu:
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. 7574-7583 - Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang:
Lipschitz Generative Adversarial Nets. 7584-7593 - Mo Zhou, Tianyi Liu, Yan Li, Dachao Lin, Enlu Zhou, Tuo Zhao:
Toward Understanding the Importance of Noise in Training Neural Networks. 7594-7602 - Hongpeng Zhou, Minghao Yang, Jun Wang, Wei Pan:
BayesNAS: A Bayesian Approach for Neural Architecture Search. 7603-7613 - Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, Tom Goldstein:
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets. 7614-7623 - Chun Jiang Zhu, Sabine Storandt, Kam-yiu Lam, Song Han, Jinbo Bi:
Improved Dynamic Graph Learning through Fault-Tolerant Sparsification. 7624-7633 - Yuqing Zhu, Yu-Xiang Wang:
Poission Subsampled Rényi Differential Privacy. 7634-7642 - Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama:
Learning Classifiers for Target Domain with Limited or No Labels. 7643-7653 - Zhanxing Zhu, Jingfeng Wu, Bing Yu, Lei Wu, Jinwen Ma:
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects. 7654-7663 - Zhenxun Zhuang, Ashok Cutkosky, Francesco Orabona:
Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization. 7664-7672 - Zachary M. Ziegler, Alexander M. Rush:
Latent Normalizing Flows for Discrete Sequences. 7673-7682 - Julian Zimmert, Haipeng Luo, Chen-Yu Wei:
Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously. 7683-7692 - Luisa M. Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson:
Fast Context Adaptation via Meta-Learning. 7693-7702 - Tijana Zrnic, Moritz Hardt:
Natural Analysts in Adaptive Data Analysis. 7703-7711
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