6 citations · 13 across the 4 of their papers we have counts for
4 papers
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…
Momentum-based variance-reduced proximal stochastic gradient method for composite nonconvex stochastic optimization
Yangyang Xu, Yibo Xu
Stochastic gradient methods (SGMs) have been extensively used for solving stochastic problems or large-scale machine learning problems. Recent works employ various techniques to im…
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…
Katyusha Acceleration for Convex Finite-Sum Compositional Optimization
Yibo Xu, Yangyang Xu
Structured problems arise in many applications. To solve these problems, it is important to leverage the structure information. This paper focuses on convex problems with a finite-…