Feb. 15, 2024, 5:42 a.m. | Mira J\"urgens, Nis Meinert, Viktor Bengs, Eyke H\"ullermeier, Willem Waegeman

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.09056v1 Announce Type: cross
Abstract: Trustworthy ML systems should not only return accurate predictions, but also a reliable representation of their uncertainty. Bayesian methods are commonly used to quantify both aleatoric and epistemic uncertainty, but alternative approaches, such as evidential deep learning methods, have become popular in recent years. The latter group of methods in essence extends empirical risk minimization (ERM) for predicting second-order probability distributions over outcomes, from which measures of epistemic (and aleatoric) uncertainty can be extracted. This …

abstract arxiv bayesian become cs.ai cs.lg deep learning popular predictions representation systems trustworthy type uncertainty

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