● Zhouchen Lin, Huan Li and Cong Fang, Accelerated Optimization for Machine Learning-First-Order Algorithms. Springer, 2020.
● Zhouchen Lin, Huan Li and Cong Fang, Alternating Direction Method of Multipliers for Machine Learning. Springer, 2022.
● Huan Li, Zhouchen Lin and Yongchun Fang, Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization. Journal of Machine Learning Research (JMLR), 23(222):1-41, 2022. (CCF A)
● Huan Li and Zhouchen Lin, Restarted Nonconvex Accelerated Gradient Descent: No More Polylogarithmic Factor in the O(ϵ −7/4 ) Complexity. International Conference on Machine Learning (ICML), 2022. (CCF A)
● Huan Li, Cong Fang and Zhouchen Lin, Accelerated First-Order Optimization Algorithms for Machine Learning. Proceedings of the IEEE, 108(11):2067-2082, 2020. (CCF A)
● Huan Li and Zhouchen Lin, On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent. Journal of Machine Learning Research (JMLR), 21(33):1-45, 2020. (CCF A)
● Huan Li and Zhouchen Lin, Revisiting EXTRA for Smooth Distributed Optimization. SIAM Journal on Optimization (SIOPT), 30(3):1795-1821, 2020.
● Huan Li, Cong Fang, Wotao Yin and Zhouchen Lin,Decentralized Accelerated Gradient Methods With Increasing Penalty Parameters. IEEE transactions on Signal Processing (TSP), 68:4855-4870, 2020.
● Huan Li and Zhouchen Lin, Accelerated Proximal Gradient Methods for Nonconvex Programming. Advances in Neural Information Processing Systems (NIPS), 2015. (CCF A)