The purpose of this paper is to look into how central notions in statistical learning theory, such as realisability, generalise under the assumption that train and test distribution are issued from the same credal set, i.e., a convex set of probability distributions. This can be considered as a first step towards a more general treatment of statistical learning under epistemic uncertainty.

翻译：本文旨在探讨统计学习理论中的核心概念（如可实现性）如何在训练分布与测试分布源于同一可信集（即概率分布的凸集）的假设下进行推广。这可以视为向认知不确定性下统计学习更一般化处理迈出的第一步。