66 citations · 72 across the 2 of their papers we have counts for
5 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…
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction
Boyue Li, Shicong Cen, Yuxin Chen +1
There is growing interest in large-scale machine learning and optimization over decentralized networks, e.g. in the context of multi-agent learning and federated learning. Due to t…
Inference and Uncertainty Quantification for Noisy Matrix Completion
Yuxin Chen, Jianqing Fan, Cong Ma +1
Noisy matrix completion aims at estimating a low-rank matrix given only partial and corrupted entries. Despite substantial progress in designing efficient estimation algorithms, it…
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
Yuxin Chen, Yuejie Chi, Jianqing Fan +2
This paper studies noisy low-rank matrix completion: given partial and noisy entries of a large low-rank matrix, the goal is to estimate the underlying matrix faithfully and effici…