Accelerating Stochastic Gradient Descent Using Antithetic Sampling. - Jingchang Liu; Linli Xu (CoRR, 2018)

Asynchronous Stochastic Composition Optimization with Variance Reduction. - Shuheng Shen; Linli Xu; Jingchang Liu; Junliang Guo; Qing Ling (CoRR, 2018)

Adaptive Proximal Average Based Variance Reducing Stochastic Methods for Optimization with Composite Regularization. - Jingchang Liu; Linli Xu; Junliang Guo; Xin Sheng (AAAI, 2019)

SPINE: Structural Identity Preserved Inductive Network Embedding. - Junliang Guo; Linli Xu; Jingchang Liu (IJCAI, 2019)

Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent. - Shuheng Shen; Linli Xu; Jingchang Liu; Xianfeng Liang; Yifei Cheng 002 (IJCAI, 2019)

Variance Reduced Local SGD with Lower Communication Complexity. - Xianfeng Liang; Shuheng Shen; Jingchang Liu; Zhen Pan; Enhong Chen; Yifei Cheng 002 (CoRR, 2019)

An accelerated variance reducing stochastic method with Douglas-Rachford splitting. - Jingchang Liu; Linli Xu; Shuheng Shen; Qing Ling (Mach. Learn., 2019)

IntroVNMT: An Introspective Model for Variational Neural Machine Translation. - Xin Sheng; Linli Xu; Junliang Guo; Jingchang Liu; Ruoyu Zhao; Yinlong Xu (AAAI, 2020)

STL-SGD: Speeding Up Local SGD with Stagewise Communication Period. - Shuheng Shen; Yifei Cheng 002; Jingchang Liu; Linli Xu (AAAI, 2021)

Communication-efficient federated learning with stagewise training strategy. - Yifei Cheng 002; Shuheng Shen; Xianfeng Liang; Jingchang Liu; Joya Chen; Tie Zhang; Enhong Chen (Neural Networks, 2023)