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ORCID35th COLT 2022: London, UK
- Po-Ling Loh, Maxim Raginsky:
Conference on Learning Theory, 2-5 July 2022, London, UK. Proceedings of Machine Learning Research 178, PMLR 2022 - Sinho Chewi, Murat A. Erdogdu, Mufan (Bill) Li, Ruoqi Shen, Shunshi Zhang:
Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev. 1-2 - Itay Safran, Jason D. Lee:
Optimization-Based Separations for Neural Networks. 3-64 - Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev, Murat A. Erdogdu:
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance. 65-102 - Tristan Milne, Adrian I. Nachman:
Wasserstein GANs with Gradient Penalty Compute Congested Transport. 103-129 - Jayadev Acharya, Ayush Jain, Gautam Kamath, Ananda Theertha Suresh, Huanyu Zhang:
Robust Estimation for Random Graphs. 130-166 - Xizhi Liu, Sayan Mukherjee:
Tight query complexity bounds for learning graph partitions. 167-181 - Julian Zimmert, Naman Agarwal, Satyen Kale:
Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. 182-226 - Laura Tinsi, Arnak S. Dalalyan:
Risk bounds for aggregated shallow neural networks using Gaussian priors. 227-253 - Gaspard Beugnot, Julien Mairal, Alessandro Rudi:
On the Benefits of Large Learning Rates for Kernel Methods. 254-282 - Daniel J. Hsu, Clayton Hendrick Sanford, Rocco A. Servedio, Emmanouil-Vasileios Vlatakis-Gkaragkounis
:
Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals. 283-312 - Matthew Faw, Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari, Sanjay Shakkottai, Rachel A. Ward:
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance. 313-355 - Yeshwanth Cherapanamjeri, Nilesh Tripuraneni, Peter L. Bartlett, Michael I. Jordan:
Optimal Mean Estimation without a Variance. 356-357 - Andrew J. Wagenmaker, Max Simchowitz, Kevin Jamieson:
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning. 358-418 - Eric Balkanski, Oussama Hanguir, Shatian Wang:
Learning Low Degree Hypergraphs. 419-420 - Carles Domingo-Enrich:
Depth and Feature Learning are Provably Beneficial for Neural Network Discriminators. 421-447 - Ohad Shamir:
The Implicit Bias of Benign Overfitting. 448-478 - Moïse Blanchard, Romain Cosson:
Universal Online Learning with Bounded Loss: Reduction to Binary Classification. 479-495 - Christopher Criscitiello, Nicolas Boumal:
Negative curvature obstructs acceleration for strongly geodesically convex optimization, even with exact first-order oracles. 496-542 - Jibang Wu, Haifeng Xu, Fan Yao:
Multi-Agent Learning for Iterative Dominance Elimination: Formal Barriers and New Algorithms. 543 - Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman:
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. 544-572 - Clément L. Canonne, Ayush Jain, Gautam Kamath, Jerry Li:
The Price of Tolerance in Distribution Testing. 573-624 - Yuval Dagan, Gil Kur:
A bounded-noise mechanism for differential privacy. 625-661 - Dan Tsir Cohen, Aryeh Kontorovich:
Learning with metric losses. 662-700 - Adam Klukowski:
Rate of Convergence of Polynomial Networks to Gaussian Processes. 701-722 - Pravesh Kothari, Pasin Manurangsi, Ameya Velingker:
Private Robust Estimation by Stabilizing Convex Relaxations. 723-777 - Ahmet Alacaoglu, Yura Malitsky:
Stochastic Variance Reduction for Variational Inequality Methods. 778-816 - Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani:
Self-Consistency of the Fokker Planck Equation. 817-841 - Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer:
Monotone Learning. 842-866 - Nicolas Christianson, Tinashe Handina, Adam Wierman:
Chasing Convex Bodies and Functions with Black-Box Advice. 867-908 - Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan:
ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. 909-981 - Liyu Chen, Haipeng Luo, Aviv Rosenberg:
Policy Optimization for Stochastic Shortest Path. 982-1046 - Rajai Nasser, Stefan Tiegel:
Optimal SQ Lower Bounds for Learning Halfspaces with Massart Noise. 1047-1074 - Hassan Ashtiani, Christopher Liaw:
Private and polynomial time algorithms for learning Gaussians and beyond. 1075-1076 - Moïse Blanchard:
Universal Online Learning: an Optimistically Universal Learning Rule. 1077-1125 - Prateek Varshney, Abhradeep Thakurta, Prateek Jain:
(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping. 1126-1166 - Xiyang Liu, Weihao Kong, Sewoong Oh:
Differential privacy and robust statistics in high dimensions. 1167-1246 - Ilias Zadik, Min Jae Song, Alexander S. Wein, Joan Bruna:
Lattice-Based Methods Surpass Sum-of-Squares in Clustering. 1247-1248 - Gal Vardi, Gilad Yehudai, Ohad Shamir:
Width is Less Important than Depth in ReLU Neural Networks. 1249-1281 - Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan:
Computational-Statistical Gap in Reinforcement Learning. 1282-1302 - Etienne Boursier, Mikhail Konobeev, Nicolas Flammarion:
Trace norm regularization for multi-task learning with scarce data. 1303-1327 - Jayadev Acharya, Clément L. Canonne, Himanshu Tyagi, Ziteng Sun:
The Role of Interactivity in Structured Estimation. 1328-1355 - Boris Muzellec, Kanji Sato, Mathurin Massias, Taiji Suzuki:
Dimension-free convergence rates for gradient Langevin dynamics in RKHS. 1356-1420 - Shinji Ito, Taira Tsuchiya, Junya Honda:
Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds. 1421-1422 - Arpit Agarwal, Sanjeev Khanna, Prathamesh Patil:
A Sharp Memory-Regret Trade-off for Multi-Pass Streaming Bandits. 1423-1462 - Buddhima Gamlath, Silvio Lattanzi, Ashkan Norouzi-Fard, Ola Svensson:
Approximate Cluster Recovery from Noisy Labels. 1463-1509 - Gil Kur, Eli Putterman:
An Efficient Minimax Optimal Estimator For Multivariate Convex Regression. 1510-1546 - Tor Lattimore:
Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret. 1547-1575 - Haipeng Luo, Mengxiao Zhang, Peng Zhao:
Adaptive Bandit Convex Optimization with Heterogeneous Curvature. 1576-1612 - Xiang Li, Jiadong Liang, Xiangyu Chang, Zhihua Zhang:
Statistical Estimation and Online Inference via Local SGD. 1613-1661 - Vincent Cohen-Addad, Frederik Mallmann-Trenn, David Saulpic:
Community Recovery in the Degree-Heterogeneous Stochastic Block Model. 1662-1692 - Nazar Buzun, Nikolay Shvetsov, Dmitry V. Dylov:
Strong Gaussian Approximation for the Sum of Random Vectors. 1693-1715 - Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin:
Smoothed Online Learning is as Easy as Statistical Learning. 1716-1786 - Erwin Bolthausen, Shuta Nakajima, Nike Sun, Changji Xu:
Gardner formula for Ising perceptron models at small densities. 1787-1911 - Pierre C. Bellec, Yiwei Shen:
Derivatives and residual distribution of regularized M-estimators with application to adaptive tuning. 1912-1947 - Sivakanth Gopi, Yin Tat Lee, Daogao Liu:
Private Convex Optimization via Exponential Mechanism. 1948-1989 - Wei Huang, Richard Combes, Cindy Trinh:
Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information. 1990-2012 - Peter L. Bartlett, Piotr Indyk, Tal Wagner:
Generalization Bounds for Data-Driven Numerical Linear Algebra. 2013-2040 - Sinho Chewi, Patrik R. Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet:
The query complexity of sampling from strongly log-concave distributions in one dimension. 2041-2059 - Wenlong Mou, Ashwin Pananjady, Martin J. Wainwright, Peter L. Bartlett:
Optimal and instance-dependent guarantees for Markovian linear stochastic approximation. 2060-2061 - Aditya Varre, Nicolas Flammarion:
Accelerated SGD for Non-Strongly-Convex Least Squares. 2062-2126 - Loucas Pillaud-Vivien, Julien Reygner, Nicolas Flammarion:
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation. 2127-2159 - Joe Suk, Samory Kpotufe:
Tracking Most Significant Arm Switches in Bandits. 2160-2182 - Julia Gaudio, Miklós Z. Rácz, Anirudh Sridhar:
Exact Community Recovery in Correlated Stochastic Block Models. 2183-2241 - Rentian Yao, Xiaohui Chen, Yun Yang:
Mean-field nonparametric estimation of interacting particle systems. 2242-2275 - Meena Jagadeesan, Ilya P. Razenshteyn, Suriya Gunasekar:
Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm. 2276-2325 - Dan Garber, Ben Kretzu:
New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guarantees. 2326-2359 - Yair Carmon, Oliver Hinder:
Making SGD Parameter-Free. 2360-2389 - Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant:
Efficient Convex Optimization Requires Superlinear Memory. 2390-2430 - Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan:
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales. 2431-2540 - Sitan Chen, Jerry Li, Ryan O'Donnell:
Toward Instance-Optimal State Certification With Incoherent Measurements. 2541-2596 - Yuval Dagan, Anthimos Vardis Kandiros, Constantinos Daskalakis:
EM's Convergence in Gaussian Latent Tree Models. 2597-2667 - Spencer Frei, Niladri S. Chatterji, Peter L. Bartlett:
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data. 2668-2703 - Alekh Agarwal, Tong Zhang:
Minimax Regret Optimization for Robust Machine Learning under Distribution Shift. 2704-2729 - Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee:
Offline Reinforcement Learning with Realizability and Single-policy Concentrability. 2730-2775 - Alekh Agarwal, Tong Zhang:
Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling. 2776-2814 - Allen Liu, Ankur Moitra:
Learning GMMs with Nearly Optimal Robustness Guarantees. 2815-2895 - Krishna Balasubramanian, Sinho Chewi, Murat A. Erdogdu, Adil Salim, Shunshi Zhang:
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo. 2896-2923 - Jikai Jin, Suvrit Sra:
Understanding Riemannian Acceleration via a Proximal Extragradient Framework. 2924-2962 - Jun Liu, Ye Yuan:
On Almost Sure Convergence Rates of Stochastic Gradient Methods. 2963-2983 - Yongxin Chen, Sinho Chewi, Adil Salim, Andre Wibisono:
Improved analysis for a proximal algorithm for sampling. 2984-3014 - Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Realizable Learning is All You Need. 3015-3069 - Yury Makarychev, Naren Sarayu Manoj, Max Ovsiankin:
Streaming Algorithms for Ellipsoidal Approximation of Convex Polytopes. 3070-3093 - Allen Liu, Mark Sellke:
The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication. 3094 - Jennifer Tang:
Minimax Regret on Patterns Using Kullback-Leibler Divergence Covering. 3095-3112 - Shivam Gupta, Eric Price:
Sharp Constants in Uniformity Testing via the Huber Statistic. 3113-3192 - Parikshit Gopalan, Michael P. Kim, Mihir Singhal, Shengjia Zhao:
Low-Degree Multicalibration. 3193-3234 - Taylan Kargin, Sahin Lale, Kamyar Azizzadenesheli, Animashree Anandkumar, Babak Hassibi:
Thompson Sampling Achieves $\tilde{O}(\sqrt{T})$ Regret in Linear Quadratic Control. 3235-3284 - Julian Zimmert, Tor Lattimore:
Return of the bias: Almost minimax optimal high probability bounds for adversarial linear bandits. 3285-3312 - Amit Attia, Tomer Koren:
Uniform Stability for First-Order Empirical Risk Minimization. 3313-3332 - Ingvar M. Ziemann, Henrik Sandberg, Nikolai Matni:
Single Trajectory Nonparametric Learning of Nonlinear Dynamics. 3333-3364 - Tom F. Sterkenburg:
On characterizations of learnability with computable learners. 3365-3379 - Matan Schliserman, Tomer Koren:
Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond. 3380-3394 - Max Hahn-Klimroth, Noëla Müller:
Near optimal efficient decoding from pooled data. 3395-3409 - Simon Buchholz:
Kernel interpolation in Sobolev spaces is not consistent in low dimensions. 3410-3440 - Haoyu Wang, Yihong Wu, Jiaming Xu, Israel Yolou:
Random Graph Matching in Geometric Models: the Case of Complete Graphs. 3441-3488 - Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi, Yunzong Xu:
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation. 3489 - Yingli Ran, Zhao Zhang, Shaojie Tang:
Improved Parallel Algorithm for Minimum Cost Submodular Cover Problem. 3490-3502 - Mohammad Reza Karimi, Ya-Ping Hsieh, Panayotis Mertikopoulos, Andreas Krause:
The Dynamics of Riemannian Robbins-Monro Algorithms. 3503 - Renato Paes Leme, Chara Podimata, Jon Schneider:
Corruption-Robust Contextual Search through Density Updates. 3504-3505 - Tomer Berg, Or Ordentlich, Ofer Shayevitz:
On The Memory Complexity of Uniformity Testing. 3506-3523 - Gábor Lugosi, Gergely Neu:
Generalization Bounds via Convex Analysis. 3524-3546 - Oren Mangoubi, Yikai Wu, Satyen Kale, Abhradeep Thakurta, Nisheeth K. Vishnoi:
Private Matrix Approximation and Geometry of Unitary Orbits. 3547-3588 - Asaf B. Cassel, Alon Cohen, Tomer Koren:
Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics. 3589-3604 - Theophile Thiery, Justin Ward:
Two-Sided Weak Submodularity for Matroid Constrained Optimization and Regression. 3605-3634 - Haipeng Luo, Mengxiao Zhang, Peng Zhao, Zhi-Hua Zhou:
Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits. 3635-3684 - Max Dabagia, Santosh S. Vempala, Christos H. Papadimitriou:
Assemblies of neurons learn to classify well-separated distributions. 3685-3717 - Enrique B. Nueve, Rafael M. Frongillo, Jessica Finocchiaro:
The Structured Abstain Problem and the Lovász Hinge. 3718-3740 - Jingqiu Ding, Tommaso d'Orsi, Chih-Hung Liu, David Steurer, Stefan Tiegel:
Fast algorithm for overcomplete order-3 tensor decomposition. 3741-3799 - Elena Grigorescu, Brendan Juba, Karl Wimmer, Ning Xie:
Hardness of Maximum Likelihood Learning of DPPs. 3800-3819 - Anastasios Tsiamis, Ingvar M. Ziemann, Manfred Morari, Nikolai Matni, George J. Pappas:
Learning to Control Linear Systems can be Hard. 3820-3857 - Zihan Zhang, Xiangyang Ji, Simon S. Du:
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies. 3858-3904 - Hongjie Chen, Tommaso d'Orsi:
On the well-spread property and its relation to linear regression. 3905-3935 - Ilias Diakonikolas, Daniel M. Kane, Yuxin Sun:
Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models. 3936-3978 - Shyam Narayanan:
Private High-Dimensional Hypothesis Testing. 3979-4027 - Itay Evron, Edward Moroshko, Rachel A. Ward, Nathan Srebro, Daniel Soudry:
How catastrophic can catastrophic forgetting be in linear regression? 4028-4079 - Daniel Freund, Thodoris Lykouris, Wentao Weng:
Efficient decentralized multi-agent learning in asymmetric queuing systems. 4080-4084 - Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Chris Ryan:
Online Learning to Transport via the Minimal Selection Principle. 4085-4109 - Elad Romanov, Tamir Bendory, Or Ordentlich:
On the Role of Channel Capacity in Learning Gaussian Mixture Models. 4110-4159 - Andrew Jacobsen, Ashok Cutkosky:
Parameter-free Mirror Descent. 4160-4211 - Eugenio Clerico, Amitis Shidani, George Deligiannidis, Arnaud Doucet:
Chained generalisation bounds. 4212-4257 - Ilias Diakonikolas, Daniel Kane:
Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise. 4258-4282 - Chirag Gupta, Aaditya Ramdas:
Faster online calibration without randomization: interval forecasts and the power of two choices. 4283-4309 - Andrea Montanari, Basil Saeed:
Universality of empirical risk minimization. 4310-4312 - Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, Nikos Zarifis:
Learning a Single Neuron with Adversarial Label Noise via Gradient Descent. 4313-4361 - Yujia Jin, Aaron Sidford, Kevin Tian:
Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods. 4362-4415 - Milad Sefidgaran, Amin Gohari, Gaël Richard, Umut Simsekli:
Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms. 4416-4463 - Jack J. Mayo, Hédi Hadiji, Tim van Erven:
Scale-free Unconstrained Online Learning for Curved Losses. 4464-4497 - Maria-Florina Balcan, Avrim Blum, Steve Hanneke, Dravyansh Sharma:
Robustly-reliable learners under poisoning attacks. 4498-4534 - Ilias Diakonikolas, Daniel Kane:
Non-Gaussian Component Analysis via Lattice Basis Reduction. 4535-4547 - Noah Golowich, Ankur Moitra:
Can Q-learning be Improved with Advice? 4548-4619 - Blake E. Woodworth, Francis R. Bach, Alessandro Rudi:
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares. 4620-4642 - Sepehr Assadi, Vaggos Chatziafratis, Jakub Lacki, Vahab Mirrokni, Chen Wang:
Hierarchical Clustering in Graph Streams: Single-Pass Algorithms and Space Lower Bounds. 4643-4702 - Ilias Diakonikolas, Daniel M. Kane, Sushrut Karmalkar, Ankit Pensia, Thanasis Pittas:
Robust Sparse Mean Estimation via Sum of Squares. 4703-4763 - Amin Coja-Oghlan, Oliver Gebhard, Max Hahn-Klimroth, Alexander S. Wein, Ilias Zadik:
Statistical and Computational Phase Transitions in Group Testing. 4764-4781 - Emmanuel Abbe, Enric Boix Adserà, Theodor Misiakiewicz:
The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks. 4782-4887 - Lechao Xiao:
Eigenspace Restructuring: A Principle of Space and Frequency in Neural Networks. 4888-4944 - Frederic Koehler, Holden Lee, Andrej Risteski:
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. 4945-4988 - Gavin Brown, Mark Bun, Adam D. Smith:
Strong Memory Lower Bounds for Learning Natural Models. 4989-5029 - Guy Blanc, Jane Lange, Ali Malik, Li-Yang Tan:
On the power of adaptivity in statistical adversaries. 5030-5061 - Yonathan Efroni, Dylan J. Foster, Dipendra Misra, Akshay Krishnamurthy, John Langford:
Sample-Efficient Reinforcement Learning in the Presence of Exogenous Information. 5062-5127 - Simina Brânzei, Jiawei Li:
The Query Complexity of Local Search and Brouwer in Rounds. 5128-5145 - Dhruv Malik, Yuanzhi Li, Aarti Singh:
Complete Policy Regret Bounds for Tallying Bandits. 5146-5174 - Qinghua Liu, Alan Chung, Csaba Szepesvári, Chi Jin:
When Is Partially Observable Reinforcement Learning Not Scary? 5175-5220 - Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan:
Strategizing against Learners in Bayesian Games. 5221-5252 - Lang Liu, Carlos Cinelli, Zaïd Harchaoui:
Orthogonal Statistical Learning with Self-Concordant Loss. 5253-5277 - Alberto Del Pia, Mingchen Ma, Christos Tzamos:
Clustering with Queries under Semi-Random Noise. 5278-5313 - Zakaria Mhammedi:
Efficient Projection-Free Online Convex Optimization with Membership Oracle. 5314-5390 - Daogao Liu:
Better Private Algorithms for Correlation Clustering. 5391-5412 - Alexandru Damian, Jason D. Lee, Mahdi Soltanolkotabi:
Neural Networks can Learn Representations with Gradient Descent. 5413-5452 - Matus Telgarsky:
Stochastic linear optimization never overfits with quadratically-bounded losses on general data. 5453-5488 - Simon Weissmann, Ashia Wilson, Jakob Zech:
Multilevel Optimization for Inverse Problems. 5489-5524 - Kangjie Zhou, Andrea Montanari:
High-Dimensional Projection Pursuit: Outer Bounds and Applications to Interpolation in Neural Networks. 5525-5527 - Chen Cheng, John C. Duchi, Rohith Kuditipudi:
Memorize to generalize: on the necessity of interpolation in high dimensional linear regression. 5528-5560 - Zakaria Mhammedi, Alexander Rakhlin:
Damped Online Newton Step for Portfolio Selection. 5561-5595 - Nima Anari, Thuy-Duong Vuong:
From Sampling to Optimization on Discrete Domains with Applications to Determinant Maximization. 5596-5618
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