8 citations · 39 across the 19 of their papers we have counts for
6 papers · 1 filter
Learning Stochastic Shortest Path with Linear Function Approximation
Yifei Min, Jiafan He, Tianhao Wang +1
We study the stochastic shortest path (SSP) problem in reinforcement learning with linear function approximation, where the transition kernel is represented as a linear mixture of…
What Happens after SGD Reaches Zero Loss? --A Mathematical Framework
Zhiyuan Li, Tianhao Wang, Sanjeev Arora
Understanding the implicit bias of Stochastic Gradient Descent (SGD) is one of the key challenges in deep learning, especially for overparametrized models, where the local minimize…
Approximate Message Passing for orthogonally invariant ensembles: Multivariate non-linearities and spectral initialization
Xinyi Zhong, Tianhao Wang, Zhou Fan
We study a class of Approximate Message Passing (AMP) algorithms for symmetric and rectangular spiked random matrix models with orthogonally invariant noise. The AMP iterates have…
Maximum likelihood for high-noise group orbit estimation and single-particle cryo-EM
Zhou Fan, Roy R. Lederman, Yi Sun +2
Motivated by applications to single-particle cryo-electron microscopy (cryo-EM), we study several problems of function estimation in a high noise regime, where samples are observed…
Variance-Aware Off-Policy Evaluation with Linear Function Approximation
Yifei Min, Tianhao Wang, Dongruo Zhou +1
We study the off-policy evaluation (OPE) problem in reinforcement learning with linear function approximation, which aims to estimate the value function of a target policy based on…
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints
Tianhao Wang, Dongruo Zhou, Quanquan Gu
We study reinforcement learning (RL) with linear function approximation under the adaptivity constraint. We consider two popular limited adaptivity models: the batch learning model…