| 2022 | Differential privacy and robust statistics in high dimensions. | Xiyang Liu, Weihao Kong, Sewoong Oh |
| 2022 | When Is Partially Observable Reinforcement Learning Not Scary? | Qinghua Liu, Alan Chung, Csaba Szepesvri, Chi Jin |
| 2022 | Orthogonal Statistical Learning with Self-Concordant Loss. | Lang Liu, Carlos Cinelli, Zad Harchaoui |
| 2022 | Better Private Algorithms for Correlation Clustering. | Daogao Liu |
| 2022 | ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm. | Chris Junchi Li, Wenlong Mou, Martin J. Wainwright, Michael I. Jordan |
| 2022 | Statistical Estimation and Online Inference via Local SGD. | Xiang Li, Jiadong Liang, Xiangyu Chang, Zhihua Zhang |
| 2022 | Corruption-Robust Contextual Search through Density Updates. | Renato Paes Leme, Chara Podimata, Jon Schneider |
| 2022 | Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini's Regret. | Tor Lattimore |
| 2022 | An Efficient Minimax Optimal Estimator For Multivariate Convex Regression. | Gil Kur, Eli Putterman |
| 2022 | Private Robust Estimation by Stabilizing Convex Relaxations. | Pravesh Kothari, Pasin Manurangsi, Ameya Velingker |
| 2022 | Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. | Frederic Koehler, Holden Lee, Andrej Risteski |
| 2022 | Rate of Convergence of Polynomial Networks to Gaussian Processes. | Adam Klukowski |
| 2022 | Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales. | Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan |
| 2022 | The Dynamics of Riemannian Robbins-Monro Algorithms. | Mohammad Reza Karimi, Ya-Ping Hsieh, Panayotis Mertikopoulos, Andreas Krause |
| 2022 | Thompson Sampling Achieves $\tilde{O}(\sqrt{T})$ Regret in Linear Quadratic Control. | Taylan Kargin, Sahin Lale, Kamyar Azizzadenesheli, Animashree Anandkumar, Babak Hassibi |
| 2022 | Computational-Statistical Gap in Reinforcement Learning. | Daniel Kane, Sihan Liu, Shachar Lovett, Gaurav Mahajan |
| 2022 | A Private and Computationally-Efficient Estimator for Unbounded Gaussians. | Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman |
| 2022 | Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods. | Yujia Jin, Aaron Sidford, Kevin Tian |
| 2022 | Understanding Riemannian Acceleration via a Proximal Extragradient Framework. | Jikai Jin, Suvrit Sra |
| 2022 | Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm. | Meena Jagadeesan, Ilya P. Razenshteyn, Suriya Gunasekar |
| 2022 | Parameter-free Mirror Descent. | Andrew Jacobsen, Ashok Cutkosky |
| 2022 | Adversarially Robust Multi-Armed Bandit Algorithm with Variance-Dependent Regret Bounds. | Shinji Ito, Taira Tsuchiya, Junya Honda |
| 2022 | Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information. | Wei Huang, Richard Combes, Cindy Trinh |
| 2022 | Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals. | Daniel J. Hsu, Clayton Hendrick Sanford, Rocco A. Servedio, Emmanouil-Vasileios Vlatakis-Gkaragkounis |
| 2022 | Realizable Learning is All You Need. | Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan |