Papers
1,396 papers found
On the Performance of Empirical Risk Minimization with Smoothed Data
Adam Block, Alexander Rakhlin, Abhishek Shetty
On the sample complexity of parameter estimation in logistic regression with normal design
Daniel Hsu, Arya Mazumdar
Open Problem: Anytime Convergence Rate of Gradient Descent
Guy Kornowski, Ohad Shamir
Open Problem: Black-Box Reductions and Adaptive Gradient Methods for Nonconvex Optimization
Xinyi Chen, Elad Hazan
Open Problem: Can Local Regularization Learn All Multiclass Problems?
Julian Asilis, Siddartha Devic, Shaddin Dughmi et al.
Open problem: Convergence of single-timescale mean-field Langevin descent-ascent for two-player zero-sum games
Guillaume Wang, Lénaïc Chizat
Open problem: Direct Sums in Learning Theory
Steve Hanneke, Shay Moran, Waknine Tom
Open Problem: Optimal Rates for Stochastic Decision-Theoretic Online Learning Under Differentially Privacy
Bingshan Hu, Nishant A. Mehta
Open Problem: What is the Complexity of Joint Differential Privacy in Linear Contextual Bandits?
Achraf Azize, Debabrota Basu
Optimal Multi-Distribution Learning
Zihan Zhang, Wenhao Zhan, Yuxin Chen et al.
Optimal score estimation via empirical Bayes smoothing
Andre Wibisono, Yihong Wu, Kaylee Yingxi Yang
Optimistic Information Directed Sampling
Gergely Neu, Matteo Papini, Ludovic Schwartz
Optimistic Rates for Learning from Label Proportions
Gene Li, Lin Chen, Adel Javanmard et al.
Oracle-Efficient Hybrid Online Learning with Unknown Distribution
Changlong Wu, Jin Sima, Wojciech Szpankowski
Physics-informed machine learning as a kernel method
Nathan Doumèche, Francis Bach, Gérard Biau et al.
Prediction from compression for models with infinite memory, with applications to hidden Markov and renewal processes
Yanjun Han, Tianze Jiang, Yihong Wu
Principal eigenstate classical shadows
Daniel Grier, Hakop Pashayan, Luke Schaeffer
Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs
Davide Maran, Alberto Maria Metelli, Matteo Papini et al.
Provable Advantage in Quantum PAC Learning
Wilfred Salmon, Sergii Strelchuk, Tom Gur
Pruning is Optimal for Learning Sparse Features in High-Dimensions
Nuri Mert Vural, Murat A Erdogdu
Reconstructing the Geometry of Random Geometric Graphs (Extended Abstract)
Han Huang, Pakawut Jiradilok, Elchanan Mossel
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation (Extended Abstract)
Yan Dai, Qiwen Cui, Simon S. Du
Regularization and Optimal Multiclass Learning
Julian Asilis, Siddartha Devic, Shaddin Dughmi et al.