Generalising realisability in statistical learning theory under
  epistemic uncertainty
Fabio Cuzzolin
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.