Credal predictors are models that are aware of epistemic uncertainty and produce a convex set of probabilistic predictions. They offer a principled way to quantify predictive epistemic uncertainty (EU) and have been shown to improve model robustness in various settings. However, most state-of-the-art methods mainly define EU as disagreement caused by random training initializations, which mostly reflects sensitivity to optimization randomness rather than uncertainty from deeper sources. To address this, we define EU as disagreement among models trained with varying relaxations of the i.i.d. assumption between training and test data. Based on this idea, we propose CreDRO, which learns an ensemble of plausible models through distributionally robust optimization. As a result, CreDRO captures EU not only from training randomness but also from meaningful disagreement due to potential distribution shifts between training and test data. Empirical results show that CreDRO consistently outperforms existing credal methods on tasks such as out-of-distribution detection across multiple benchmarks and selective classification in medical applications.

翻译：信念预测器是能够认知不确定性并生成凸集概率预测的模型。它们为量化预测认知不确定性提供了一种严谨方法，并已被证明能提升模型在多种场景下的鲁棒性。然而，现有主流方法主要将认知不确定性定义为随机训练初始化导致的预测分歧，这更多反映的是优化随机性的敏感度而非深层不确定性来源。为解决此问题，我们将认知不确定性定义为在训练数据与测试数据之间独立同分布假设的不同松弛程度下训练模型所产生的预测分歧。基于这一思想，我们提出CreDRO框架，通过分布鲁棒优化学习一组可信模型集成。实验表明，CreDRO不仅能捕获训练随机性带来的认知不确定性，还能捕捉因训练-测试数据潜在分布偏移产生的实质性分歧。在多个基准数据集上的分布外检测任务以及医学应用中的选择性分类任务中，CreDRO始终显著优于现有信念方法。