Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach. - Haibo Yang 001; Xin Zhang 054; Minghong Fang; Jia Liu 002 (CDC, 2019)

Adaptive Multi-Hierarchical signSGD for Communication-Efficient Distributed Optimization. - Haibo Yang 001; Xin Zhang 054; Minghong Fang; Jia Liu 002 (SPAWC, 2020)

Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning. - Haibo Yang 001; Minghong Fang; Jia Liu 002 (ICLR, 2021)

STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning. - Prashant Khanduri; Pranay Sharma; Haibo Yang 001; Mingyi Hong 001; Jia Liu 002; Ketan Rajawat; Pramod K. Varshney (NeurIPS, 2021)

CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning. - Haibo Yang 001; Jia Liu 002; Elizabeth S. Bentley (WiOpt, 2021)

Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach. - Prashant Khanduri; Haibo Yang 001; Mingyi Hong 001; Jia Liu 002; Hoi-To Wai; Sijia Liu 001 (ICLR, 2022)

Anarchic Federated Learning. - Haibo Yang 001; Xin Zhang 054; Prashant Khanduri; Jia Liu 002 (ICML, 2022)

Over-the-Air Federated Learning with Joint Adaptive Computation and Power Control. - Haibo Yang 001; Peiwen Qiu; Jia Liu 002; Aylin Yener (ISIT, 2022)

NET-FLEET: achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data. - Xin Zhang 054; Minghong Fang; Zhuqing Liu; Haibo Yang 001; Jia Liu 002; Zhengyuan Zhu (MobiHoc, 2022)

SAGDA: Achieving $ mathcal{O}( epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning. - Haibo Yang 001; Zhuqing Liu; Xin Zhang 054; Jia Liu 002 (NeurIPS, 2022)

Taming Fat-Tailed ("Heavier-Tailed" with Potentially Infinite Variance) Noise in Federated Learning. - Haibo Yang 001; Peiwen Qiu; Jia Liu 002 (NeurIPS, 2022)

CHARLES: Channel-Quality-Adaptive Over-the-Air Federated Learning over Wireless Networks. - Jiayu Mao; Haibo Yang 001; Peiwen Qiu; Jia Liu 002; Aylin Yener (SPAWC, 2022)

SAGDA: Achieving O(ε-2) Communication Complexity in Federated Min-Max Learning. - Haibo Yang 001; Zhuqing Liu; Xin Zhang 054; Jia Liu 002 (CoRR, 2022)

Federated Multi-Objective Learning. - Haibo Yang 001; Zhuqing Liu; Jia Liu 002; Chaosheng Dong; Michinari Momma (NeurIPS, 2023)

Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation. - Haibo Yang 001; Peiwen Qiu; Prashant Khanduri; Minghong Fang; Jia Liu 002 (ICML, 2024)

Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning. - Tianchen Zhou; Hairi; Haibo Yang 001; Jia Liu 002; Tian Tong; Fan Yang 084; Michinari Momma; Yan Gao (ICML, 2024)

Adversarial Attacks to Multi-Modal Models. - Zhihao Dou; Xin Hu; Haibo Yang 001; Zhuqing Liu; Minghong Fang (LAMPS@CCS, 2024)

Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning? - Peizhong Ju; Haibo Yang 001; Jia Liu 002; Yingbin Liang; Ness B. Shroff (MobiHoc, 2024)

Do We Really Need to Design New Byzantine-robust Aggregation Rules? - Minghong Fang; Seyedsina Nabavirazavi; Zhuqing Liu; Wei Sun 013; Sundaraja Sitharama Iyengar; Haibo Yang 001 (NDSS, 2025)

STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning. - Zhuqing Liu; Chaosheng Dong; Michinari Momma; Simone Shao; Shaoyuan Xu; Yan Gao; Haibo Yang 001; Jia Liu 002 (UAI, 2025)

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach. - Hairi; Jiao Yang; Tianchen Zhou; Haibo Yang 001; Chaosheng Dong; Fan Yang 084; Michinari Momma; Yan Gao; Jia Liu 002 (CoRR, 2025)