JUCAL: Jointly Calibrating Aleatoric and Epistemic Uncertainty in Classification Tasks

Jakob Heiss, Sören Lambrecht, Jakob Weissteiner +4

We study post-calibration uncertainty for trained ensembles of classifiers. Specifically, we consider both aleatoric (label noise) and epistemic (model) uncertainty. Among the most…