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
Models can be retrained as new data arrive, but deploying every new version risks replacing a good policy with a worse one. We study how to plan policy updates (i.e., meta-policy) before future candidates are trained, balancing the benefits of improvement against the risk of performance regression. Our offline meta-pol...
LatentCP is developed, a prior-free conformal framework that constructs uncertainty sets for latent distributional parameters using only observed context--response pairs and a specified forward model, and maintains nominal latent coverage under weak forward identification, observational nonidentifiability, and latent h...
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.
