Speaker：Changliang Zou (Nankai University)

Time：2025-12-11 16:30

Location：Conference Room C802 at Administration Building at Haiyun Campus

Abstract:

In decision-making under uncertainty, Contextual Robust Optimization (CRO) provides reliability by minimizing the worst-case decision loss over a prediction set, hedging against label variability. While recent advances use conformal prediction to construct prediction sets for machine learning models, the downstream decisions depend critically on the choice of conformal sets. To address this, we introduce novel model selection framework named Conformalized Robust Optimization with Model Selection (CROMS) that unifies robustness control with decision risk minimization. Furthermore, since traditional coverage is a sufficient but not necessary condition for robustness, enforcing such constraints often leads to overly conservative decisions. To overcome this limitation, we develop a remedy named Conformal Robustness Control (CRC), that directly optimizes the prediction set construction under explicit robustness constraints, thereby enabling more efficient decisions without compromising robustness.