378 citations · 1.8k across the 48 of their papers we have counts for
9 papers · 1 filter
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…
Learning Mixtures of Low-Rank Models
Yanxi Chen, Cong Ma, H. Vincent Poor +1
We study the problem of learning mixtures of low-rank models, i.e. reconstructing multiple low-rank matrices from unlabelled linear measurements of each. This problem enriches two…
Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution under Random Designs
Yuxin Chen, Jianqing Fan, Bingyan Wang +1
We investigate the effectiveness of convex relaxation and nonconvex optimization in solving bilinear systems of equations under two different designs (i.e.a sort of random Fouri…
Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization
Shicong Cen, Chen Cheng, Yuxin Chen +2
Natural policy gradient (NPG) methods are among the most widely used policy optimization algorithms in contemporary reinforcement learning. This class of methods is often applied i…
Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality
Changxiao Cai, H. Vincent Poor, Yuxin Chen
We study the distribution and uncertainty of nonconvex optimization for noisy tensor completion -- the problem of estimating a low-rank tensor given incomplete and corrupted observ…
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction
Gen Li, Yuting Wei, Yuejie Chi +2
Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a Markov decision process (MDP), based on a single trajectory of Markovian samples induce…