most citedUnlocking Accuracy and Fairness in Differentially Private Image Classification

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stat.ML2024

Conformalized Credal Regions for Classification with Ambiguous Ground Truth

Michele Caprio, David Stutz, Shuo Li +1

An open question in \emph{Imprecise Probabilistic Machine Learning} is how to empirically derive a credal region (i.e., a closed and convex family of probabilities on the output sp…

stat.ML2024

Conformalized Credal Set Predictors

Alireza Javanmardi, David Stutz, Eyke Hüllermeier

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 attr…