9 citations · 13 across the 7 of their papers we have counts for
5 papers · 1 filter
Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data
Yonggui Yan, Jie Chen, Pin-Yu Chen +3
We first propose a decentralized proximal stochastic gradient tracking method (DProxSGT) for nonconvex stochastic composite problems, with data heterogeneously distributed on multi…
Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization
Gabriel Mancino-Ball, Shengnan Miao, Yangyang Xu +1
Consider a network of decentralized computing agents collaboratively solving a nonconvex stochastic composite problem. In this work, we propose a single-loop algorithm, called…
Distributed stochastic inertial-accelerated methods with delayed derivatives for nonconvex problems
Yangyang Xu, Yibo Xu, Yonggui Yan +1
Stochastic gradient methods (SGMs) are predominant approaches for solving stochastic optimization. On smooth nonconvex problems, a few acceleration techniques have been applied to…
A Decentralized Primal-Dual Framework for Non-convex Smooth Consensus Optimization
Gabriel Mancino-Ball, Yangyang Xu, Jie Chen
In this work, we introduce ADAPD, ecentrlized rimal-ual algorithmic framework for solving non-convex and smooth consens…
Parallel and distributed asynchronous adaptive stochastic gradient methods
Yangyang Xu, Yibo Xu, Yonggui Yan +3
Stochastic gradient methods (SGMs) are the predominant approaches to train deep learning models. The adaptive versions (e.g., Adam and AMSGrad) have been extensively used in practi…