提出基于认知不确定性拒绝预测的新框架，解决小样本下模型自信误判问题。
Epistemic Reject Option Prediction
- 以最小化预期后悔为目标，重新定义最优预测器
- 在数据不足区域主动拒绝预测，避免高风险决策
- 适合医疗、金融等小样本高风险场景的可信预测
在高风险应用中，预测模型不仅需准确，还需量化并传达不确定性。拒绝选项预测通过在不确定时放弃预测来应对这一需求。传统方法仅关注随机不确定性，该假设在训练数据充足时成立，但在实际中小样本场景中不现实。本文提出认知不确定性拒绝预测器，在数据不足导致认知不确定性高的区域主动拒绝预测。基于贝叶斯学习，将最优预测器重新定义为最小化预期后悔（即当前模型与完全掌握数据分布的贝叶斯最优预测器之间的性能差距）的预测器。当某输入的后悔值超过预设拒绝成本时，模型选择拒绝。据我们所知，这是首个能识别训练数据不足以支撑可靠预测的输入的原理性框架。
原文摘要 · Abstract (English)
In high-stakes applications, predictive models must not only produce accurate predictions but also quantify and communicate their uncertainty. Reject-option prediction addresses this by allowing the model to abstain when prediction uncertainty is high. Traditional reject-option approaches focus solely on aleatoric uncertainty, an assumption valid only when large training data makes the epistemic uncertainty negligible. However, in many practical scenarios, limited data makes this assumption unrealistic. This paper introduces the epistemic reject-option predictor, which abstains in regions of high epistemic uncertainty caused by insufficient data. Building on Bayesian learning, we redefine the optimal predictor as the one that minimizes expected regret -- the performance gap between the learned model and the Bayes-optimal predictor with full knowledge of the data distribution. The model abstains when the regret for a given input exceeds a specified rejection cost. To our knowledge, this is the first principled framework that enables learning predictors capable of identifying inputs for which the available training data is insufficient to support well-informed predictions.
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