Wang, D., Liu, W., and Mao, X. (2026) "BRFedTD: A Novel Framework for Federated Reinforcement Learning with Byzantine-Resilient Policy Evaluation". IEEE Transactions on Pattern Analysis and Machine Intelligence, To appear.
Wang, D., Liu, W., and Mao, X. (2026) "Byzantine-Resilient Decentralized Optimization for Joint Feature Selection in Multi-Task Networks". IEEE Transactions on Signal Processing, 74, 1926-1941.
Liu, W., and Mao, X. (2026) "Fast Decentralized Median Value Estimation". IEEE Transactions on Networking, 34, 3334-3345.
Mao, X., Wang, H., and Zhang, X. (2026) "Efficient Quantization Mean Estimation for Distributed Learning". Journal of Computational and Graphical Statistics, 35(2), 830–839.
Wang, Z., Mao, X., Kim, J. K., and Wang, H. (2026) "Selection Bias Adjustment by Functional Transfer Learning via Reproducing Kernel Hilbert Space". Journal of the Business and Economic Statistics, 44(1), 177–187.
Mao, X., Wang, H., and Wang, Z. (2025) "Group-Sparse Inductive Matrix Completion through Transfer Learning". IEEE Transactions on Information Theory, 71(11), 8673-8696.
Liu, W., Ma, J., Mao, X., and Tang, K. (2025) "A New Accelerated Off-Policy Stochastic Preconditioned TD(0) Algorithm". IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(9), 8088–8101.
Liu, W., Mao, X., and Tu, J. (2025) "Communication-Efficient Distributed Sparse Learning with Oracle Property and Geometric Convergence". Journal of the American Statistical Association, 120(552), 2606–2618.
Liu, W., Mao, X., Zhang, X., and Zhang, X. (2025) "Robust Personalized Federated Learning with Sparse Penalization". Journal of the American Statistical Association, 120(549), 266–277.
Xia, H., Liu, W., and Mao, X. (2024) "ST$_k$: A Scalable Module for Solving Top-k problems". NeurIPS, 37, 16752-16771.
Liu, W., Tu, J., Mao, X., and Chen, X. (2024) "Majority Vote for Distributed Differentially Private Sign Selection". The Annals of Statistics, 52(4), 1671-1690.
Tu, J., Liu, W., and Mao, X. (2024) "Distributed Estimation on Semi-Supervised Generalized Linear Model". Journal of Machine Learning Research, 25(76), 1–41.
Tu, J., Liu, W., Mao, X., and Xu, M. (2024) "Distributed Semi-Supervised Sparse Statistical Inference". IEEE Transactions on Information Theory, 70(6), 4197-4217.
Mao, X., Wang, H., Wang, Z., and Yang, S. (2024) "Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness". Journal of Computational and Graphical Statistics, 33(4), 1320-1328.
Liu, W., Mao, X., Zhang, X., and Zhang, X. (2024) "Efficient Sparse Least Absolute Deviation Regression with Differential Privacy". IEEE Transactions on Information Forensics and Security, 19, 2328-2339.
Liu, W., Mao, X., and Zhang, X. (2022) "Fast and Robust Sparsity Learning over Networks: A Decentralized Median Regression Approach". IEEE Transactions on Signal Processing, 70, 797-809.
Zhang, Y., Xu, M., Mao, X., and Wang, J. (2022) "Uncertainty Modeling in Generative Compressed Sensing". ICML, 26655-26668.
陈松蹊，毛晓军，王聪。(2022) "大数据情境下的数据完备化：挑战与对策"。 《管理世界》，第38卷，第1期，第196-207页。
Tu, J., Liu, W., Mao, X., and Chen, X. (2021) "Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference". Journal of Machine Learning Research, 22(84), 1–67.
Mao, X., Wong, R. K. W., and Chen, S. X. (2021) "Matrix Completion under Low-Rank Missing Mechanism". Statistica Sinica, 31(4), 2005–2030.
Wang, J., Wong, R. K. W., Mao, X., and Chan, K. C. G. (2021) "Matrix Completion with Model-free Weighting". ICML, 10927-10936.
Chen, X., Liu, W., Mao, X., and Yang, Z. (2020) "Distributed High-Dimensional Regression under a Quantile Loss Function". Journal of Machine Learning Research, 21(182), 1–43.
Liu, W., Mao, X., and Wong, R. K. W. (2020) "Median Matrix Completion: from Embarrassment to Optimality". ICML, 6294-6304.
Mao, X., Chen, S. X., and Wong, R. K. W. (2019) "Matrix Completion with Covariate Information". Journal of the American Statistical Association, 114(525), 198-210.
