9 citations · 13 across the 7 of their papers we have counts for
7 papers
LoDAdaC: a unified local training-based decentralized framework with adaptive gradients and compressed communication
Wei Liu, Anweshit Panda, Ujwal Pandey +5
In the decentralized distributed learning, achieving fast convergence and low communication cost is essential for scalability and high efficiency. Adaptive gradient methods, such a…
Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization
Wei Liu, Anweshit Panda, Ujwal Pandey +6
In this paper, we design two compressed decentralized algorithms for solving nonconvex stochastic optimization under two different scenarios. Both algorithms adopt a momentum techn…
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…