When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making

• URL: http://arxiv.org/abs/2510.07750v1
• Date: Thu, 09 Oct 2025 03:38:17 GMT
• Title: When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making
• Authors: Wenbin Zhou, Shixiang Zhu,
• Abstract summary: We propose a new framework that provides distribution-free, finite-sample guarantees on miscoverage and regret.<n>Our method constructs valid estimators that trace out the mis-regret frontier.<n>These results offer the first principled data-driven methodology for guiding robustness selection.
• Score: 8.234618636958462
• License: http://creativecommons.org/licenses/by/4.0/
• 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 than existing approaches. These results offer the first principled data-driven methodology for guiding robustness selection and empower practitioners to balance robustness and conservativeness in high-stakes decision-making.

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