11 citations · 31 across the 6 of their papers we have counts for
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cs.LG2018
A Model Parallel Proximal Stochastic Gradient Algorithm for Partially Asynchronous Systems
Rui Zhu, Di Niu
Large models are prevalent in modern machine learning scenarios, including deep learning, recommender systems, etc., which can have millions or even billions of parameters. Paralle…
cs.LG2018★ 6 cited
A Block-wise, Asynchronous and Distributed ADMM Algorithm for General Form Consensus Optimization
Rui Zhu, Di Niu, Zongpeng Li
Many machine learning models, including those with non-smooth regularizers, can be formulated as consensus optimization problems, which can be solved by the alternating direction m…
cs.LG2018★ 4 cited
Asynchronous Stochastic Proximal Methods for Nonconvex Nonsmooth Optimization
Rui Zhu, Di Niu, Zongpeng Li
We study stochastic algorithms for solving nonconvex optimization problems with a convex yet possibly nonsmooth regularizer, which find wide applications in many practical machine…