arXiv stat.ML
· Papers
Quantification of Credal Uncertainty: A Distance-Based Approach
arXiv:2603.27270v2 Announce Type: replace-cross Abstract: Credal sets, i.e., closed convex sets of probability measures, provide a natural framework to represent aleatoric and epistemic uncertainty in machine learning. Yet how to quantify these two types of uncertainty for a given credal set, particularly in multiclass