Generalising realisability in statistical learning theory under epistemic uncertainty

• URL: http://arxiv.org/abs/2402.14759v1
• Date: Thu, 22 Feb 2024 18:20:25 GMT
• Title: Generalising realisability in statistical learning theory under epistemic uncertainty
• Abstract summary: This paper looks 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.
• Score: 5.7205053622241735
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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