Distributionally Robust Learning and Optimization
Robust statistical procedures often lead to minimax, nonconvex, or large-scale optimization problems that are difficult to solve with generic algorithms. My work studies the structure created by distributional robustness, invariance, and heterogeneous data, and uses it to design computational methods that make these statistical targets practical without losing their interpretation or theoretical guarantees.
Related work 5
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Xiong, X., Guo, Z., and Cai, T. (2026).
Guided Uncertainty-Aware Robust Domain Transfer.
Technical Report
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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]
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Koo, T. and Guo, Z. (2025).
Synthetic Control with Weight Uncertainty: Robust Identification and Statistical Inference.
Technical Report
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Guo, Z., Wang, Z., Hu, Y., and Bach, F. (2025).
Statistical Analysis of Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss.
Technical Report
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Wang, Z.#, Hu, Y.#, Bühlmann, P.✉, and Guo, Z.✉ (2024).
Causal Invariance Learning via Efficient Nonconvex Optimization.
Technical Report