GRAND: A Gradient-Related Ascent and Descent Algorithmic Framework for Minimax Problems. - Xiaochun Niu; Ermin Wei (Allerton, 2022)

DISH: A Distributed Hybrid Primal-Dual Optimization Framework to Utilize System Heterogeneity. - Xiaochun Niu; Ermin Wei (CDC, 2022)

FedHybrid: A Hybrid Federated Optimization Method for Heterogeneous Clients. - Xiaochun Niu; Ermin Wei (IEEE Trans. Signal Process., 2023)

Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters. - Zhenyu Sun; Xiaochun Niu; Ermin Wei (AISTATS, 2024)

Exact Community Recovery in the Geometric SBM. - Julia Gaudio; Xiaochun Niu; Ermin Wei (SODA, 2024)

Exact Label Recovery in Euclidean Random Graphs. - Julia Gaudio; Charlie K. Guan; Xiaochun Niu; Ermin Wei (CoRR, 2024)

Collaborative Learning with Shared Linear Representations: Statistical Rates and Optimal Algorithms. - Xiaochun Niu; Lili Su; Jiaming Xu 002; Pengkun Yang (CoRR, 2024)

DISH: A Distributed Hybrid Optimization Method Leveraging System Heterogeneity. - Xiaochun Niu; Ermin Wei (IEEE Trans. Signal Process., 2024)

Incentive Analysis for Agent Participation in Federated Learning. - Lihui Yi; Xiaochun Niu; Ermin Wei (CoRR, 2025)