让模型预测的误差在不同输入下更准确，提升决策可靠性。
Calibrating conditional risk
- 将条件风险校准转化为标准回归任务，统一建模思路。
- 理论证明其与概率校准相关但独立，具独特性。
- 在学习延迟决策框架中验证有效性，适合高风险场景研究者。
我们提出并研究了条件风险校准问题，即估计预测模型在给定输入特征下的期望损失。在分类和回归设置中分析该问题，发现其本质上等价于标准回归任务。在分类场景下，进一步揭示条件风险校准与个体/条件概率校准之间的联系，并提供性能度量的理论洞察。结果表明，尽管条件风险校准与现有不确定性量化问题有关，但仍是一个独立且自成体系的机器学习问题。实验上，我们验证了理论结论，并展示了其在学习延迟决策（L2D）框架中的实际意义。系统性实验提供了定性和定量评估，为未来不确定性感知决策研究提供指导。
原文摘要 · Abstract (English)
We introduce and study the problem of calibrating conditional risk, which involves estimating the expected loss of a prediction model conditional on input features. We analyze this problem in both classification and regression settings and show that it is fundamentally equivalent to a standard regression task. For classification settings, we further establish a connection between conditional risk calibration and individual/conditional probability calibration, and develop theoretical insights for the performance metric. This reveals that while conditional risk calibration is related to existing uncertainty quantification problems, it remains a distinct and standalone machine learning problem. Empirically, we validate our theoretical findings and demonstrate the practical implications of conditional risk calibration in the learning to defer (L2D) framework. Our systematic experiments provide both qualitative and quantitative assessments, offering guidance for future research in uncertainty-aware decision-making.
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