Papers
1,396 papers found
The Implicit Bias of Benign Overfitting
Ohad Shamir
The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks
Emmanuel Abbe, Enric Boix Adsera, Theodor Misiakiewicz
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance
Matthew Faw, Isidoros Tziotis, Constantine Caramanis et al.
The Price of Tolerance in Distribution Testing
Clement L Canonne, Ayush Jain, Gautam Kamath et al.
The Query Complexity of Local Search and Brouwer in Rounds
Simina Branzei, Jiawei Li
The query complexity of sampling from strongly log-concave distributions in one dimension
Sinho Chewi, Patrik R Gerber, Chen Lu et al.
The Role of Interactivity in Structured Estimation
Jayadev Acharya, Clement L. Canonne, Ziteng Sun et al.
The Structured Abstain Problem and the Lovász Hinge
Jessica J Finocchiaro, Rafael Frongillo, Enrique B Nueve
Thompson Sampling Achieves $\tilde{O}(\sqrt{T})$ Regret in Linear Quadratic Control
Taylan Kargin, Sahin Lale, Kamyar Azizzadenesheli et al.
Tight query complexity bounds for learning graph partitions
Xizhi Liu, Sayan Mukherjee
Toward Instance-Optimal State Certification With Incoherent Measurements
Sitan Chen, Jerry Li, Ryan O’Donnell
Towards a Theory of Non-Log-Concave Sampling:First-Order Stationarity Guarantees for Langevin Monte Carlo
Krishna Balasubramanian, Sinho Chewi, Murat A Erdogdu et al.
Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information
Wei Huang, Richard Combes, Cindy Trinh
Trace norm regularization for multi-task learning with scarce data
Etienne Boursier, Mikhail Konobeev, Nicolas Flammarion
Tracking Most Significant Arm Switches in Bandits
Joe Suk, Samory Kpotufe
Two-Sided Weak Submodularity for Matroid Constrained Optimization and Regression
Theophile Thiery, Justin Ward
Understanding Riemannian Acceleration via a Proximal Extragradient Framework
Jikai Jin, Suvrit Sra
Uniform Stability for First-Order Empirical Risk Minimization
Amit Attia, Tomer Koren
Universality of empirical risk minimization
Andrea Montanari, Basil N. Saeed
Universal Online Learning with Bounded Loss: Reduction to Binary Classification
Moise Blanchard, Romain Cosson
Wasserstein GANs with Gradient Penalty Compute Congested Transport
Tristan Milne, Adrian I Nachman
When Is Partially Observable Reinforcement Learning Not Scary?
Qinghua Liu, Alan Chung, Csaba Szepesvari et al.
Width is Less Important than Depth in ReLU Neural Networks
Gal Vardi, Gilad Yehudai, Ohad Shamir