Asynchronous Stochastic Frank-Wolfe Algorithms for Non-Convex Optimization. - Bin Gu 001; Wenhan Xian; Heng Huang (IJCAI, 2019)

A Unified q-Memorization Framework for Asynchronous Stochastic Optimization. - Bin Gu 001; Wenhan Xian; Zhouyuan Huo; Cheng Deng; Heng Huang (J. Mach. Learn. Res., 2020)

Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning. - Wenhan Xian; Feihu Huang 001; Heng Huang (AAAI, 2021)

Learning Better Visual Data Similarities via New Grouplet Non-Euclidean Embedding. - Yanfu Zhang; Lei Luo 001; Wenhan Xian; Heng Huang (ICCV, 2021)

A Faster Decentralized Algorithm for Nonconvex Minimax Problems. - Wenhan Xian; Feihu Huang 001; Yanfu Zhang; Heng Huang (NeurIPS, 2021)

Communication-Efficient Adam-Type Algorithms for Distributed Data Mining. - Wenhan Xian; Feihu Huang 001; Heng Huang (ICDM, 2022)

Doubly Sparse Asynchronous Learning for Stochastic Composite Optimization. - Runxue Bao; Xidong Wu; Wenhan Xian; Heng Huang (IJCAI, 2022)

Distributed Dynamic Safe Screening Algorithms for Sparse Regularization. - Runxue Bao; Xidong Wu; Wenhan Xian; Heng Huang (CoRR, 2022)

Finding Local Minima Efficiently in Decentralized Optimization. - Wenhan Xian; Heng Huang (NeurIPS, 2023)

Delving into the Convergence of Generalized Smooth Minimax Optimization. - Wenhan Xian; Ziyi Chen 002; Heng Huang (ICML, 2024)

Efficient Optimization Algorithms for Nonconvex Machine Learning Problems. - Wenhan Xian (2024)