Papers
1,396 papers found
Minimax Regret Optimization for Robust Machine Learning under Distribution Shift
Alekh Agarwal, Tong Zhang
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance
Nuri Mert Vural, Lu Yu, Krishna Balasubramanian et al.
Monotone Learning
Olivier J Bousquet, Amit Daniely, Haim Kaplan et al.
Multi-Agent Learning for Iterative Dominance Elimination: Formal Barriers and New Algorithms
Jibang Wu, Haifeng Xu, Fan Yao
Multilevel Optimization for Inverse Problems
Simon Weissmann, Ashia Wilson, Jakob Zech
(Nearly) Optimal Private Linear Regression for Sub-Gaussian Data via Adaptive Clipping
Prateek Varshney, Abhradeep Thakurta, Prateek Jain
Near optimal efficient decoding from pooled data
Max Hahn-Klimroth, Noela Müller
Near-Optimal Statistical Query Hardness of Learning Halfspaces with Massart Noise
Ilias Diakonikolas, Daniel Kane
Near-Optimal Statistical Query Lower Bounds for Agnostically Learning Intersections of Halfspaces with Gaussian Marginals
Daniel J Hsu, Clayton H Sanford, Rocco Servedio et al.
Negative curvature obstructs acceleration for strongly geodesically convex optimization, even with exact first-order oracles
Christopher Criscitiello, Nicolas Boumal
Neural Networks can Learn Representations with Gradient Descent
Alexandru Damian, Jason Lee, Mahdi Soltanolkotabi
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares
Blake Woodworth, Francis Bach, Alessandro Rudi
Non-Gaussian Component Analysis via Lattice Basis Reduction
Ilias Diakonikolas, Daniel Kane
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
Dylan J Foster, Akshay Krishnamurthy, David Simchi-Levi et al.
Offline Reinforcement Learning with Realizability and Single-policy Concentrability
Wenhao Zhan, Baihe Huang, Audrey Huang et al.
On Almost Sure Convergence Rates of Stochastic Gradient Methods
Jun Liu, Ye Yuan
On characterizations of learnability with computable learners
Tom F. Sterkenburg
Online Learning to Transport via the Minimal Selection Principle
Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang et al.
On the Benefits of Large Learning Rates for Kernel Methods
Gaspard Beugnot, Julien Mairal, Alessandro Rudi
On The Memory Complexity of Uniformity Testing
Tomer Berg, Or Ordentlich, Ofer Shayevitz
On the power of adaptivity in statistical adversaries
Guy Blanc, Jane Lange, Ali Malik et al.
On the Role of Channel Capacity in Learning Gaussian Mixture Models
Elad Romanov, Tamir Bendory, Or Ordentlich