| 2023 | On Testing and Learning Quantum Junta Channels. | Zongbo Bao, Penghui Yao |
| 2023 | Proper Losses, Moduli of Convexity, and Surrogate Regret Bounds. | Han Bao |
| 2023 | Multitask Learning via Shared Features: Algorithms and Hardness. | Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou |
| 2023 | Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes. | Pranjal Awasthi, Nika Haghtalab, Eric Zhao |
| 2023 | Online Learning and Solving Infinite Games with an ERM Oracle. | Angelos Assos, Idan Attias, Yuval Dagan, Constantinos Daskalakis, Maxwell K. Fishelson |
| 2023 | Private Online Prediction from Experts: Separations and Faster Rates. | Hilal Asi, Vitaly Feldman, Tomer Koren, Kunal Talwar |
| 2023 | Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression. | Gabriel Arpino, Ramji Venkataramanan |
| 2023 | Intrinsic dimensionality and generalization properties of the R-norm inductive bias. | Navid Ardeshir, Daniel J. Hsu, Clayton Hendrick Sanford |
| 2023 | Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling. | Jason M. Altschuler, Kunal Talwar |
| 2023 | Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning. | Zeyuan Allen-Zhu, Yuanzhi Li |
| 2023 | Differentially Private and Lazy Online Convex Optimization. | Naman Agarwal, Satyen Kale, Karan Singh, Abhradeep Thakurta |
| 2023 | VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation. | Alekh Agarwal, Yujia Jin, Tong Zhang |
| 2023 | Causal Matrix Completion. | Anish Agarwal, Munther A. Dahleh, Devavrat Shah, Dennis Shen |
| 2023 | Provable Benefits of Representational Transfer in Reinforcement Learning. | Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang |
| 2023 | The One-Inclusion Graph Algorithm is not Always Optimal. | Ishaq Aden-Ali, Yeshwanth Cherapanamjeri, Abhishek Shetty, Nikita Zhivotovskiy |
| 2023 | SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics. | Emmanuel Abbe, Enric Boix Adser, Theodor Misiakiewicz |
| 2023 | From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks. | Luca Arnaboldi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro |
| 2023 | Finite-Sample Symmetric Mean Estimation with Fisher Information Rate. | Shivam Gupta, Jasper C. H. Lee, Eric Price |
| 2023 | Testing of Index-Invariant Properties in the Huge Object Model. | Sourav Chakraborty, Eldar Fischer, Arijit Ghosh, Gopinath Mishra, Sayantan Sen |
| 2022 | Return of the bias: Almost minimax optimal high probability bounds for adversarial linear bandits. | Julian Zimmert, Tor Lattimore |
| 2022 | Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States. | Julian Zimmert, Naman Agarwal, Satyen Kale |
| 2022 | Single Trajectory Nonparametric Learning of Nonlinear Dynamics. | Ingvar M. Ziemann, Henrik Sandberg, Nikolai Matni |
| 2022 | High-Dimensional Projection Pursuit: Outer Bounds and Applications to Interpolation in Neural Networks. | Kangjie Zhou, Andrea Montanari |
| 2022 | Offline Reinforcement Learning with Realizability and Single-policy Concentrability. | Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee |
| 2022 | Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies. | Zihan Zhang, Xiangyang Ji, Simon S. Du |