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An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
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. In this work, we provide a formal way of representing uncertainty in contin
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