Nonsmooth Composite Nonconvex-Concave Minimax Optimization. - Jiajin Li; Linglingzhi Zhu; Anthony Man-Cho So (CoRR, 2022)

LogSpecT: Feasible Graph Learning Model from Stationary Signals with Recovery Guarantees. - Shangyuan Liu; Linglingzhi Zhu; Anthony Man-Cho So (NeurIPS, 2023)

Universal Gradient Descent Ascent Method for Nonconvex-Nonconcave Minimax Optimization. - Taoli Zheng; Linglingzhi Zhu; Anthony Man-Cho So; Jose H. Blanchet; Jiajin Li (NeurIPS, 2023)

Riemannian linearized proximal algorithms for nonnegative inverse eigenvalue problem. - Sangho Kum; Chong Li 002; Jinhua Wang 001; Jen-Chih Yao; Linglingzhi Zhu (Numer. Algorithms, 2023)

Differentially Private Online Federated Learning with Correlated Noise. - Jiaojiao Zhang; Linglingzhi Zhu; Mikael Johansson 001 (CDC, 2024)

Shuffling Gradient Descent-Ascent with Variance Reduction for Nonconvex-Strongly Concave Smooth Minimax Problems. - Xia Jiang; Linglingzhi Zhu; Anthony Man-Cho So; Shisheng Cui; Jian Sun 003 (CoRR, 2024)

Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces. - Linglingzhi Zhu; Yao Xie (CoRR, 2024)

Single-Loop Variance-Reduced Stochastic Algorithm for Nonconvex-Concave Minimax Optimization. - Xia Jiang; Linglingzhi Zhu; Taoli Zheng; Anthony Man-Cho So (ICASSP, 2025)

Locally Differentially Private Online Federated Learning With Correlated Noise. - Jiaojiao Zhang; Linglingzhi Zhu; Dominik Fay; Mikael Johansson 001 (IEEE Trans. Signal Process., 2025)