A reliable representation of uncertainty is essential for the application of modern machine learning methods in safety-critical settings. In this regard, the use of credal sets (i.e., convex sets of probability distributions) has recently been proposed as a suitable approach to representing epistemic uncertainty. However, as with other approaches to epistemic uncertainty, training credal predictors is computationally complex and usually involves (re-)training an ensemble of models. The resulting computational complexity prevents their adoption for complex models such as foundation models and multi-modal systems. To address this problem, we propose an efficient method for credal prediction that is grounded in the notion of relative likelihood and inspired by techniques for the calibration of probabilistic classifiers. For each class label, our method predicts a range of plausible probabilities in the form of an interval. To produce the lower and upper bounds of these intervals, we propose a technique that we refer to as decalibration. Extensive experiments show that our method yields credal sets with strong performance across diverse tasks, including coverage-efficiency evaluation, out-of-distribution detection, and in-context learning. Notably, we demonstrate credal prediction on models such as TabPFN and CLIP -- architectures for which the construction of credal sets was previously infeasible.

翻译：在安全关键场景中应用现代机器学习方法时，可靠的不确定性表征至关重要。为此，近期研究提出使用置信集（即概率分布的凸集）作为表征认知不确定性的合适方法。然而，与其他认知不确定性方法类似，训练置信预测器计算复杂度高，通常需要（重新）训练模型集成。由此产生的计算复杂性阻碍了其在基础模型和多模态系统等复杂模型中的应用。为解决该问题，我们提出一种基于相对似然概念的置信预测高效方法，其灵感来源于概率分类器的校准技术。对于每个类别标签，我们的方法以区间形式预测一组合理的概率范围。为生成这些区间的上下界，我们提出一种称为去校准的技术。大量实验表明，我们的方法生成的置信集在覆盖效率评估、分布外检测和上下文学习等多样化任务中均表现出色。值得注意的是，我们在TabPFN和CLIP等模型上实现了置信预测——这些架构此前被认为无法构建置信集。