Multi-source Learning
Data from different studies, populations, or environments may follow different distributions, so simple pooling can obscure heterogeneity and produce fragile conclusions. My work develops transfer, federated, adversarial, and invariant-learning methods that share information across sources while preserving stability under changes in source composition and distribution shift.
Related work 12
-
Xiong, X., Guo, Z., and Cai, T. (2026).
Guided Uncertainty-Aware Robust Domain Transfer.
Technical Report
-
Xu, S., Guo, Z., Staveland, B. R., Knight, R. T., and Li, L.✉ (2026).
Robust Learning of Heterogeneous Dynamic Systems.
Technical Report
-
Xiong, X., Guo, Z., Zhu, H., Hong, C., Smoller, J. W., Cai, T., Liu, M✉. (2026).
Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI.
Technical Report
-
Xiong, X., Guo, Z.✉, and Cai, T.✉ (2026).
Guided Adversarial Robust Transfer Learning with Source Mixing
Journal of the American Statistical Association, 1–14. Published online September 8, 2026. [arXiv] [Code]
-
Wang, Z., Bühlmann, P., and Guo, Z. (2026).
Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation.
Annals of Statistics, 54(2), 570–596.
-
Wang, Z., Liu, M., Lei, J., Bach, F.✉, and Guo, Z.✉ (2025).
StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data
Technical Report
-
Gu, Y., Fang, C., Xu, Y., Guo, Z., and Fan, J. (2025).
Fundamental Computational Limits in Pursuing Invariant Causal Prediction and Invariance-Guided Regularization
Technical Report
-
Guo, Z., Li, X., Han, L., and Cai Tianxi. (2025).
Robust Inference for Federated Meta-Learning
Journal of the American Statistical Association, 120(551), 1695-1710. [Code]
-
Wang, Z., Si, N., Guo, Z., and Liu, M.✉ (2024).
Multi-source Stable Variable Importance Measure via Adversarial Machine Learning
Technical Report
-
Zhan, K., Xiong, X., Guo, Z., Cai, T., and Liu, M. (2024).
Domain Adaptation Optimized for Robustness in Mixture Populations.
Technical report
-
Guo, Z. (2024).
Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies .
Journal of the American Statistical Association, 119(547), 1968-1984. [Code]
-
Wang, X., Zhou, H., · · ·, 4CE, Avillach, P.✉, Guo, Z.✉, and Cai, T.✉ (2022).
Surv-Maximin: Robust Federated Approach to Transporting Survival Risk Prediction Models.
Journal of Biomedical Informatics, 134 (2022): 104-176.