Sanchez Gomez, J. A., Mo, W., Zhao, J., and Liu, Y. (2025). Hub detection in Gaussian Graphical Models. Journal of the American Statistical Association, to appear.
Mo, W., and Liu, Y. (2022). Efficient Learning of Optimal Individualized Treatment Rules for Heteroscedastic or Misspecified Treatment-Free Effect Models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 84(2): 440-472. DOI: 10.1111/rssb.12474.
Mo, W.*, Qi, Z*., and Liu, Y. (2021). Learning Optimal Distributionally Robust Individualized Treatment Rules (with discussion). Journal of the American Statistical Association, 116(534): 659-674. DOI: 10.1080/01621459.2020.1796359.
Mo, W.*, Qi, Z.*, and Liu, Y. (2021). Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules. Journal of the American Statistical Association, 116(534): 699-707. DOI: 10.1080/01621459.2020.1866581.
Dong, J.*, Mo, W.*, Qi, Z., Shi, C., Fang, X., and Tarokh, V. (2023). PASTA: Pessimistic assortment optimization. In Proceedings of the 40th International Conference on Machine Learning (ICML 2023), 202:8276-8295.
Mo, W., Yu, H., and Liu, C. (2021). Markdown First Reinforcement Learning Pricing with Demand Learning. In Amazon Machine Learning Conference 2021.
Mo, W., and Liu, Y. (2024). A Selective Review of Individualized Decision Making. In Statistics in Precision Health (eds Y. Zhao and D. Chen). ICSA Book Series in Statistics. Springer, Cham. DOI: 10.1007/978-3-031-50690-1_2.
Mo, W., and Liu, Y. (2021). Supervised Learning. In Wiley StatsRef: Statistics Reference Online (eds N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri and J.L. Teugels). DOI: 10.1002/9781118445112.stat08302.