110 citations · 112 across the 5 of their papers we have counts for
4 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…
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…
Adaptive Primal-Dual Stochastic Gradient Method for Expectation-constrained Convex Stochastic Programs
Yonggui Yan, Yangyang Xu
Stochastic gradient methods (SGMs) have been widely used for solving stochastic optimization problems. A majority of existing works assume no constraints or easy-to-project constra…
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…