arXiv Machine Learning July 7, 2026 By Christopher B\"ulte, Yusuf Sale, Timo L\"ohr, Paul Hofman, Gitta Kutyniok, Eyke H\"ullermeier

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression

Read the original on arXiv Machine Learning →

arXiv:2504. 18433v3 Announce Type: replace Abstract: Uncertainty quantification is crucial in machine learning, yet most (axiomatic) studies of uncertainty measures focus on classification, leaving a gap in regression settings with limited formal justification and evaluations.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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