Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends. - Lingying Huang; Xiaomeng Chen; Wei Huo 002; Jiazheng Wang; Fan Zhang 016; Bo Bai 001; Ling Shi 001 (CoRR, 2021)

Improving Primal Heuristics for Mixed Integer Programming Problems based on Problem Reduction: A Learning-based Approach. - Lingying Huang; Xiaomeng Chen; Wei Huo 002; Jiazheng Wang; Fan Zhang 016; Bo Bai 001; Ling Shi 001 (ICARCV, 2022)

Distributed Nash equilibrium seeking with stochastic event-triggered mechanism. - Wei Huo 002; Kam Fai Elvis Tsang; Yamin Yan; Karl Henrik Johansson; Ling Shi 001 (Autom., 2024)

A survey on secure decentralized optimization and learning. - Changxin Liu 001; Nicola Bastianello; Wei Huo 002; Yang Shi 001; Karl Henrik Johansson (CoRR, 2024)

Communication-Efficient and Differentially-Private Distributed Nash Equilibrium Seeking With Linear Convergence. - Xiaomeng Chen; Wei Huo 002; Kemi Ding; Subhrakanti Dey; Ling Shi 001 (IEEE Control. Syst. Lett., 2024)

Compression-Based Privacy Preservation for Distributed Nash Equilibrium Seeking in Aggregative Games. - Wei Huo 002; Xiaomeng Chen; Kemi Ding; Subhrakanti Dey; Ling Shi 001 (IEEE Control. Syst. Lett., 2024)

Neural-Network-Based Distributed Generalized Nash Equilibrium Seeking for Uncertain Nonlinear Multiagent Systems. - Wei Huo 002; Lingying Huang; Subhrakanti Dey; Ling Shi 001 (IEEE Trans. Control. Netw. Syst., 2024)

Differentially private dual gradient tracking for distributed resource allocation. - Wei Huo 002; Xiaomeng Chen; Lingying Huang; Karl Henrik Johansson; Ling Shi 001 (Autom., 2025)

Remote State Estimation With Discounted Multi-Armed Bandits for Non-Stationary Channel Selection. - Jiuzhou Zhang; Wei Huo 002; Xiaomeng Chen; Daniel E. Quevedo; Ling Shi 001 (IEEE Control. Syst. Lett., 2025)

An Efficient Distributed Nash Equilibrium Seeking With Compressed and Event-Triggered Communication. - Xiaomeng Chen; Wei Huo 002; Yuchi Wu; Subhrakanti Dey; Ling Shi 001 (IEEE Trans. Autom. Control., 2025)

Decentralized Optimization with Amplified Privacy via Efficient Communication. - Wei Huo 002; Changxin Liu 001; Kemi Ding; Karl Henrik Johansson; Ling Shi 001 (CoRR, 2025)

Federated Cubic Regularized Newton Learning with sparsification-amplified differential privacy. - Wei Huo 002; Changxin Liu 001; Kemi Ding; Karl Henrik Johansson; Ling Shi 001 (Autom., 2026)