462 citations · 754 across the 16 of their papers we have counts for
Showing 2017Show all
2 papers · 1 filter
stat.ML2017★ 7 cited
A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery
Lingxiao Wang, Xiao Zhang, Quanquan Gu
We propose a generic framework based on a new stochastic variance-reduced gradient descent algorithm for accelerating nonconvex low-rank matrix recovery. Starting from an appropria…
stat.ML2017★ 3 cited
Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements
Xiao Zhang, Lingxiao Wang, Quanquan Gu
We study the problem of estimating low-rank matrices from linear measurements (a.k.a., matrix sensing) through nonconvex optimization. We propose an efficient stochastic variance r…