29 citations · 40 across the 7 of their papers we have counts for
24 papers
Asynchronous Gradient Play in Zero-Sum Multi-agent Games
Ruicheng Ao, Shicong Cen, Yuejie Chi
Finding equilibria via gradient play in competitive multi-agent games has been attracting a growing amount of attention in recent years, with emphasis on designing efficient strate…
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games
Shicong Cen, Yuejie Chi, Simon S. Du +1
Multi-Agent Reinforcement Learning (MARL) -- where multiple agents learn to interact in a shared dynamic environment -- permeates across a wide range of critical applications. Whil…
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting
Gen Li, Yuxin Chen, Yuejie Chi +2
Low-complexity models such as linear function representation play a pivotal role in enabling sample-efficient reinforcement learning (RL). The current paper pertains to a scenario…
Beyond Procrustes: Balancing-Free Gradient Descent for Asymmetric Low-Rank Matrix Sensing
Cong Ma, Yuanxin Li, Yuejie Chi
Low-rank matrix estimation plays a central role in various applications across science and engineering. Recently, nonconvex formulations based on matrix factorization are provably…
Spectral Methods for Data Science: A Statistical Perspective
Yuxin Chen, Yuejie Chi, Jianqing Fan +1
Spectral methods have emerged as a simple yet surprisingly effective approach for extracting information from massive, noisy and incomplete data. In a nutshell, spectral methods re…
Low-Rank Matrix Recovery with Scaled Subgradient Methods: Fast and Robust Convergence Without the Condition Number
Tian Tong, Cong Ma, Yuejie Chi
Many problems in data science can be treated as estimating a low-rank matrix from highly incomplete, sometimes even corrupted, observations. One popular approach is to resort to ma…