Author

Shi-Xiang Zhu

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#machine learning Preprint Oct 2026

Calibrating Ambiguity Set via Diagnostic Transport for Distributionally Robust Optimization

Distributionally robust optimization (DRO) protects decisions against distributional uncertainty by optimizing over an ambiguity set, but poorly aligned set geometry can require large radii and yield overly conservative decisions. We introduce diagnostic-transport DRO (DT-DRO), which uses held-out calibration data to a...

Wen-Bin Zhou, Elizabeth Cucuzzella, Shi-Xiang Zhu · 0 citations

Preprint Sep 2026

Conformal-DRO: Distributionally Robust Optimization with Conformalized Ambiguity Set

Conformal-DRO is proposed, which uses nested conformal regions to construct an ambiguity set for the future latent law, which covers this law with probability at least $1-\alpha$ in finite samples, without estimating underlying latent laws or their mixing mechanism.

Lu-Hao Zhang, Shi-Xiang Zhu · 1 citation

#machine learning May 2026

Learning Polyhedral Conformal Sets for Robust Optimization

A decision-aware conformal framework that learns uncertainty sets tailored to robust optimization objectives by directly minimizing the induced robust loss, while preserving statistical validity through conformal calibration is proposed.

Shuyi Chen, Wenbin Zhou, Shi-Xiang Zhu · arXiv.org · 1 citation