228 citations · 726 across the 22 of their papers we have counts for
4 papers · 1 filter
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…
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…
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
Tian Tong, Cong Ma, Yuejie Chi
Low-rank matrix estimation is a canonical problem that finds numerous applications in signal processing, machine learning and imaging science. A popular approach in practice is to…
Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data
Yuxin Chen, Jianqing Fan, Cong Ma +1
This paper delivers improved theoretical guarantees for the convex programming approach in low-rank matrix estimation, in the presence of (1) random noise, (2) gross sparse outlier…