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Conformal-DRO: Distributionally Robust Optimization with Conformalized Ambiguity Set
Data-driven distributionally robust optimization (DRO) typically treats the conditional outcome law as fixed and uses ambiguity sets to capture estimation error. This paper studies latent distributional heterogeneity, where each instance has an unobserved law but contributes only one observation, so uncertainty persists even if the mixture law is known. We propose Conformal-DRO, which uses nested conformal regions to construct an ambiguity set for the future latent law. Under exchangeability, the set covers this law with probability at least $1-α$ in finite samples, without estimating underlyi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-10T04:30:21.000Z
First collected: 2026-09-20T19:12:12.556Z. This is not the publication date.