Conformalized Credal Set Predictors

arXiv:2402.10723v1 Announce Type: cross
Abstract: Credal sets are sets of probability distributions that are considered as candidates for an imprecisely known ground-truth distribution. In machine learning, they have recently attracted attention as an appealing formalism for uncertainty representation, in particular due to their ability to represent both the aleatoric and epistemic uncertainty in a prediction. However, the design of methods for learning credal set predictors remains a challenging problem. In this paper, we make use of conformal prediction for this …

abstract arxiv attention credal cs.lg distribution ground-truth machine machine learning prediction probability representation set stat.ml truth type uncertainty