2025/05/19 by Zhou, Wenbin, Agni Orfanoudaki, Orfanoudaki, Agni +1 · 1 citation
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Risk and Portfolio Optimization
paper · pdf · doi:10.48550/arxiv.2505.13243
openalex publication_date 2025/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In many operational settings, decision-makers must commit to actions before uncertainty resolves, but existing optimization tools rarely quantify how consistently a chosen decision remains optimal across plausible scenarios. This paper introduces CREDO -- Conformalized Risk Estimation for Decision Optimization, a distribution-free framework that quantifies the probability that a prescribed decision remains (near-)optimal across realizations of uncertainty. CREDO reformulates decision risk through the inverse feasible region -- the set of outcomes under which a decision is optimal -- and estimates its probability using inner approximations constructed from conformal prediction balls generated by a conditional generative model. This approach yields finite-sample, distribution-free lower bounds on the probability of decision optimality. The framework is model-agnostic and broadly applicable across a wide range of optimization problems. Extensive numerical experiments demonstrate that CREDO provides accurate, efficient, and reliable evaluations of decision optimality across various optimization settings.