| 2021 | Adaptive Discretization for Adversarial Lipschitz Bandits. | Chara Podimata, Alex Slivkins |
| 2021 | Learning from Censored and Dependent Data: The case of Linear Dynamics. | Orestis Plevrakis |
| 2021 | Towards a Dimension-Free Understanding of Adaptive Linear Control. | Juan C. Perdomo, Max Simchowitz, Alekh Agarwal, Peter L. Bartlett |
| 2021 | Provable Memorization via Deep Neural Networks using Sub-linear Parameters. | Sejun Park, Jaeho Lee, Chulhee Yun, Jinwoo Shin |
| 2021 | SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality. | Courtney Paquette, Kiwon Lee, Fabian Pedregosa, Elliot Paquette |
| 2021 | It was "all" for "nothing": sharp phase transitions for noiseless discrete channels. | Jonathan Niles-Weed, Ilias Zadik |
| 2021 | Information-Theoretic Generalization Bounds for Stochastic Gradient Descent. | Gergely Neu |
| 2021 | A Theory of Heuristic Learnability. | Mikito Nanashima |
| 2021 | Adversarially Robust Learning with Unknown Perturbation Sets. | Omar Montasser, Steve Hanneke, Nathan Srebro |
| 2021 | Learning to Sample from Censored Markov Random Fields. | Ankur Moitra, Elchanan Mossel, Colin Sandon |
| 2021 | Learning with invariances in random features and kernel models. | Song Mei, Theodor Misiakiewicz, Andrea Montanari |
| 2021 | Improved Analysis of the Tsallis-INF Algorithm in Stochastically Constrained Adversarial Bandits and Stochastic Bandits with Adversarial Corruptions. | Saeed Masoudian, Yevgeny Seldin |
| 2021 | Random Graph Matching with Improved Noise Robustness. | Cheng Mao, Mark Rudelson, Konstantin E. Tikhomirov |
| 2021 | The Connection Between Approximation, Depth Separation and Learnability in Neural Networks. | Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, Ohad Shamir |
| 2021 | Approximation Algorithms for Socially Fair Clustering. | Yury Makarychev, Ali Vakilian |
| 2021 | Corruption-robust exploration in episodic reinforcement learning. | Thodoris Lykouris, Max Simchowitz, Alex Slivkins, Wen Sun |
| 2021 | A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Partial Differential Equations. | Yulong Lu, Jianfeng Lu, Min Wang |
| 2021 | Exponentially Improved Dimensionality Reduction for l1: Subspace Embeddings and Independence Testing. | Yi Li, David P. Woodruff, Taisuke Yasuda |
| 2021 | Stochastic Approximation for Online Tensorial Independent Component Analysis. | Chris Junchi Li, Michael I. Jordan |
| 2021 | Structured Logconcave Sampling with a Restricted Gaussian Oracle. | Yin Tat Lee, Ruoqi Shen, Kevin Tian |
| 2021 | Mirror Descent and the Information Ratio. | Tor Lattimore, Andrs Gyrgy |
| 2021 | Improved Regret for Zeroth-Order Stochastic Convex Bandits. | Tor Lattimore, Andrs Gyrgy |
| 2021 | Projected Stochastic Gradient Langevin Algorithms for Constrained Sampling and Non-Convex Learning. | Andrew G. Lamperski |
| 2021 | Nonparametric Regression with Shallow Overparameterized Neural Networks Trained by GD with Early Stopping. | Ilja Kuzborskij, Csaba Szepesvri |
| 2021 | On the Minimal Error of Empirical Risk Minimization. | Gil Kur, Alexander Rakhlin |