13 citations · 26 across the 4 of their papers we have counts for
Showing stat.MLShow all
3 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…
stat.ML2016★ 13 cited
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation
Lingxiao Wang, Xiao Zhang, Quanquan Gu
We propose a unified framework for estimating low-rank matrices through nonconvex optimization based on gradient descent algorithm. Our framework is quite general and can be applie…