Generalising realisability in statistical learning theory under epistemic uncertainty

arXiv:2402.14759v1 Announce Type: new
Abstract: 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.

abstract arxiv credal cs.ai cs.lg distribution look math.st paper probability set statistical stat.th test theory train type uncertainty