39 citations · 69 across the 8 of their papers we have counts for
8 papers
Collaboratively Learning Linear Models with Structured Missing Data
Chen Cheng, Gary Cheng, John Duchi
We study the problem of collaboratively learning least squares estimates for agents. Each agent observes a different subset of the features$\unicode{x2013}$e.g., containing dat…
Dimension free ridge regression
Chen Cheng, Andrea Montanari
Random matrix theory has become a widely useful tool in high-dimensional statistics and theoretical machine learning. However, random matrix theory is largely focused on the propor…
How many labelers do you have? A closer look at gold-standard labels
Chen Cheng, Hilal Asi, John Duchi
The construction of most supervised learning datasets revolves around collecting multiple labels for each instance, then aggregating the labels to form a type of "true" label. We q…
Memorize to Generalize: on the Necessity of Interpolation in High Dimensional Linear Regression
Chen Cheng, John Duchi, Rohith Kuditipudi
We examine the necessity of interpolation in overparameterized models, that is, when achieving optimal predictive risk in machine learning problems requires (nearly) interpolating…
The high-dimensional asymptotics of first order methods with random data
Michael Celentano, Chen Cheng, Andrea Montanari
We study a class of deterministic flows in , parametrized by a random matrix with i.i.d. centered subgaussian…
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…
Tackling small eigen-gaps: Fine-grained eigenvector estimation and inference under heteroscedastic noise
Chen Cheng, Yuting Wei, Yuxin Chen
This paper aims to address two fundamental challenges arising in eigenvector estimation and inference for a low-rank matrix from noisy observations: (1) how to estimate an unknown…
Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed Low-Rank Matrices
Yuxin Chen, Chen Cheng, Jianqing Fan
This paper is concerned with the interplay between statistical asymmetry and spectral methods. Suppose we are interested in estimating a rank-1 and symmetric matrix $\mathbf{M}^{\s…