Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning. - Kuangyu Ding; Jingyang Li; Kim-Chuan Toh (CoRR, 2023)

On proximal augmented Lagrangian based decomposition methods for dual block-angular convex composite programming problems. - Kuangyu Ding; Xin Yee Lam; Kim-Chuan Toh (Comput. Optim. Appl., 2023)

Developing Lagrangian-based Methods for Nonsmooth Nonconvex Optimization. - Nachuan Xiao; Kuangyu Ding; Xiaoyin Hu; Kim-Chuan Toh (CoRR, 2024)

Optimization Hyper-parameter Laws for Large Language Models. - Xingyu Xie; Kuangyu Ding; Shuicheng Yan; Kim-Chuan Toh; Tianwen Wei (CoRR, 2024)

Memory-Efficient 4-bit Preconditioned Stochastic Optimization. - Jingyang Li; Kuangyu Ding; Kim-Chuan Toh; Pan Zhou 002 (CoRR, 2024)

Adam-family Methods with Decoupled Weight Decay in Deep Learning. - Kuangyu Ding; Nachuan Xiao; Kim-Chuan Toh (Trans. Mach. Learn. Res., 2025)

On exploration of an interior mirror descent flow for stochastic nonconvex constrained problem. - Kuangyu Ding; Kim-Chuan Toh (CoRR, 2025)

Stochastic Bregman Subgradient Methods for Nonsmooth Nonconvex Optimization Problems. - Kuangyu Ding; Kim-Chuan Toh (J. Optim. Theory Appl., 2025)