提出SCoRE框架，实现对模型可信预测的严格风险控制。
Conformal Selective Prediction with General Risk Control
- 基于置信推断与假设检验，构造广义e值实现风险控制。
- 在有限样本下保证可信预测中的错误率不超过设定阈值。
- 适用于药物发现、健康风险预测等高风险场景。
在部署人工智能模型时，选择性预测允许在模型置信度低时放弃预测。为实现这一承诺，必须对模型被信任的情况严格控制错误率。本文提出选择性置信风险控制与e值（SCoRE）框架，可为任意训练好的模型和任意用户定义的有界连续风险生成决策。SCoRE在系统选择信任的“正例”中提供两类风险保证。其基于置信推断与假设检验思想，首先构建一类广义e值——非负随机变量，其与未知风险的乘积期望不超过1，该性质由数据可交换性保证，无需任何建模假设。将这些e值传递至假设检验流程，即可获得具有有限样本误差控制的二元信任决策。SCoRE避免了统一集中性假设，可直接扩展至分布偏移场景。通过模拟实验及在药物发现、健康风险预测和大语言模型中的应用，验证了方法的有效性。
原文摘要 · Abstract (English)
In deploying artificial intelligence (AI) models, selective prediction offers the option to abstain from making a prediction when uncertain about model quality. To fulfill its promise, it is crucial to enforce strict and precise error control over cases where the model is trusted. We propose Selective Conformal Risk control with E-values (SCoRE), a new framework for deriving such decisions for any trained model and any user-defined, bounded and continuously-valued risk. SCoRE offers two types of guarantees on the risk among ``positive'' cases in which the system opts to trust the model. Built upon conformal inference and hypothesis testing ideas, SCoRE first constructs a class of (generalized) e-values, which are non-negative random variables whose product with the unknown risk has expectation no greater than one. Such a property is ensured by data exchangeability without requiring any modeling assumptions. Passing these e-values on to hypothesis testing procedures, we yield the binary trust decisions with finite-sample error control. SCoRE avoids the need of uniform concentration, and can be readily extended to settings with distribution shifts. We evaluate the proposed methods with simulations and demonstrate their efficacy through applications to error management in drug discovery, health risk prediction, and large language models.
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