Generalising realisability in statistical learning theory under epistemic uncertainty
This work addresses foundational issues in statistical learning theory for scenarios with epistemic uncertainty, but it is incremental as a first step toward more general frameworks.
The paper investigates how realisability in statistical learning theory generalizes when training and test distributions come from the same credal set, representing epistemic uncertainty, as an initial step toward broader treatment of learning under such uncertainty.
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.