Calibrating Decision Robustness via Inverse Conformal Risk Control

2025/10/09 by Wenbin Zhou, Zhou, Wenbin, Shixiang Zhu +1
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Reinforcement Learning in Robotics #Risk and Portfolio Optimization

paper · pdf · doi:10.48550/arxiv.2510.07750

openalex publication_date 2025/10/09 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28

Abstract

Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions. Recent approaches using conformal prediction construct data-driven uncertainty sets with finite-sample coverage guarantees, but they still fix coverage targets a priori and offer little guidance for selecting robustness levels. We propose a new framework that provides distribution-free, finite-sample guarantees on both miscoverage and regret for any family of robust predict-then-optimize policies. Our method constructs valid estimators that trace out the miscoverage--regret Pareto frontier, enabling decision-makers to reliably evaluate and calibrate robustness levels according to their cost--risk preferences. The framework is simple to implement, broadly applicable across classical optimization formulations, and achieves sharper finite-sample performance. This paper offers a principled data-driven methodology for guiding robustness selection and empowers practitioners to balance robustness and conservativeness in high-stakes decision-making.

Citations

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Related

• Conformal Uncertainty Sets for Robust Optimization
• Optimal Model Selection for Conformalized Robust Optimization
• Conformal Contextual Robust Optimization
• Conformalized Decision Risk Assessment
• The Best of Many Robustness Criteria in Decision Making: Formulation and Application to Robust Pricing