让决策模型在每步行动后都具备可验证的安全保障。
Conformal Risk-Averse Decision Making with Action Conditional Guarantee
- 引入动作条件的预测集，实现对每一步决策的精准安全约束。
- 在真实数据集上显著提升动作条件下的决策性能。
- 适合需要高可靠性、强安全保证的医疗或自动驾驶场景。
由机器学习驱动的可靠决策系统需要具备明确安全保证的不确定性量化（UQ）方法。分位数预测通过将机器学习预测结果包装成预测集，提供此类UQ；Kiyani等人（2025b）的研究表明这些预测集可转化为最优风险规避型决策策略，但仅能提供边际安全保证。本文通过三项改进：(i) 提出动作条件的分位数预测，使安全保证显式依赖于决策者所采取的每个动作；(ii) 证明动作条件预测集可作为风险规避决策者优化动作条件价值风险（action-conditional VaR）的可行决策空间代理；(iii) 提出一种基于分位数损失最小化的有限样本原则性算法，将Gibbs等人（2025）的框架与动作条件保证相连接。在两个真实数据集上的实验表明，本方法在动作条件性能上显著优于传统分位数基线。
原文摘要 · Abstract (English)
Reliable decision making pipelines powered by machine learning models require uncertainty quantification (UQ) methods that come with explicit safety guarantees. Conformal prediction provides such UQ by wrapping ML predictions into prediction sets, and recent work by Kiyani et al. (2025b) established that these sets can be translated into optimal risk-averse decision policies -- yet only inheriting marginal safety guarantees. We generalize and strengthen their results by (i) introducing action-conditional conformal prediction, which yields safety guarantees conditioned explicitly on each action taken by the decision maker, (ii) showing that action-conditional prediction sets serve as a proxy for the feasible decision space for risk-averse decision makers aiming to optimize action-conditional value-at-risk, and (iii) proposing a principled finite-sample algorithm based on pinball-loss minimization, connecting the framework of Gibbs et al. (2025) to action-conditional guarantees. Experiments on two real-world datasets confirm that our approach significantly improves action-conditional performance over conformal baselines.
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