Papers
1,396 papers found
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent
Gergely Neu, Gintare Karolina Dziugaite, Mahdi Haghifam et al.
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
Dylan Foster, Alexander Rakhlin, David Simchi-Levi et al.
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon
Zihan Zhang, Xiangyang Ji, Simon Du
It was “all” for “nothing”: sharp phase transitions for noiseless discrete channels
Jonathan Niles-Weed, Ilias Zadik
Johnson-Lindenstrauss Transforms with Best Confidence
Maciej Skorski
Kernel Thinning
Raaz Dwivedi, Lester Mackey
Last-iterate Convergence of Decentralized Optimistic Gradient Descent/Ascent in Infinite-horizon Competitive Markov Games
Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang et al.
Lazy OCO: Online Convex Optimization on a Switching Budget
Uri Sherman, Tomer Koren
Learning and testing junta distributions with sub cube conditioning
Xi Chen, Rajesh Jayaram, Amit Levi et al.
Learning from Censored and Dependent Data: The case of Linear Dynamics
Orestis Plevrakis
Learning in Matrix Games can be Arbitrarily Complex
Gabriel P. Andrade, Rafael Frongillo, Georgios Piliouras
Learning sparse mixtures of permutations from noisy information
Anindya De, Ryan O’Donnell, Rocco Servedio
Learning to Sample from Censored Markov Random Fields
Ankur Moitra, Elchanan Mossel, Colin P Sandon
Learning to Stop with Surprisingly Few Samples
Daniel Russo, Assaf Zeevi, Tianyi Zhang
Learning with invariances in random features and kernel models
Song Mei, Theodor Misiakiewicz, Andrea Montanari
Machine Unlearning via Algorithmic Stability
Enayat Ullah, Tung Mai, Anup Rao et al.
Majorizing Measures, Sequential Complexities, and Online Learning
Adam Block, Yuval Dagan, Alexander Rakhlin
Minimax Regret for Stochastic Shortest Path with Adversarial Costs and Known Transition
Liyu Chen, Haipeng Luo, Chen-Yu Wei
Mirror Descent and the Information Ratio
Tor Lattimore, Andras Gyorgy
Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks
Cong Fang, Jason Lee, Pengkun Yang et al.
Moment Multicalibration for Uncertainty Estimation
Christopher Jung, Changhwa Lee, Mallesh Pai et al.
Multiplayer Bandit Learning, from Competition to Cooperation
Simina Branzei, Yuval Peres
(Nearly) Dimension Independent Private ERM with AdaGrad Rates\{via Publicly Estimated Subspaces
Peter Kairouz, Monica Ribero Diaz, Keith Rush et al.
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
Dongruo Zhou, Quanquan Gu, Csaba Szepesvari
Near Optimal Distributed Learning of Halfspaces with Two Parties
Mark Braverman, Gillat Kol, Shay Moran et al.