5 citations · 7 across the 3 of their papers we have counts for
4 papers
Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization
Kun Yuan, Xinmeng Huang, Yiming Chen +3
Decentralized optimization is effective to save communication in large-scale machine learning. Although numerous algorithms have been proposed with theoretical guarantees and empir…
DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training
Kun Yuan, Yiming Chen, Xinmeng Huang +4
The scale of deep learning nowadays calls for efficient distributed training algorithms. Decentralized momentum SGD (DmSGD), in which each node averages only with its neighbors, is…
Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders
Xinmeng Huang, Kun Yuan, Xianghui Mao +1
When applying a stochastic algorithm, one must choose an order to draw samples. The practical choices are without-replacement sampling orders, which are empirically faster and more…
Tight Coefficients of Averaged Operators via Scaled Relative Graph
Xinmeng Huang, Ernest K. Ryu, Wotao Yin
Many iterative methods in optimization are fixed-point iterations with averaged operators. As such methods converge at an rate with the constant determined by th…