提出新方法动态校准决策鲁棒性，让风险与成本权衡更可控。
Calibrating Decision Robustness via Inverse Conformal Risk Control
- 通过逆置信风险控制构建可校准的鲁棒决策框架
- 在有限样本下保证误覆盖与后悔值的双重置信界
- 适合高风险决策场景中需要权衡保守与成本的实践者
鲁棒优化通过应对最坏情况来保护决策，但其有效性依赖于预先设定的鲁棒性水平，该水平常被随意选择，导致保护不足或过于保守且成本高昂。近期基于置信预测的方法构建了具有有限样本覆盖保证的数据驱动不确定性集，但仍需预先固定覆盖目标，且缺乏鲁棒性水平选择的指导。本文提出一种新框架，为任意一类鲁棒预测-优化策略提供分布无关、有限样本下的误覆盖与后悔值双重保证。该方法构造有效估计器，描绘出误覆盖-后悔帕累托前沿，使决策者能根据自身成本-风险偏好可靠评估并校准鲁棒性水平。该框架实现简单，广泛适用于经典优化形式，且在有限样本下表现更优。本文提供了一种原则性的数据驱动方法，指导鲁棒性选择，助力实践者在高风险决策中平衡鲁棒性与保守性。
原文摘要 · Abstract (English)
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.
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