13 citations · 26 across the 4 of their papers we have counts for
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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.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…