Thomas Mortier — accepted AI-conference papers

2023

On the Calibration of Probabilistic Classifier Sets

Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman

AISTATS 2023poster

Multi-class classification methods that produce sets of probabilistic classifiers, such as ensemble learning methods, are able to model aleatoric and epistemic uncertainty. Aleatoric uncertainty is then typically quantified via the Bayes error, and epistemic uncertainty via the size of the set. In t…

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2022

Set-valued prediction in hierarchical classification with constrained representation complexity

Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczyński, Willem Waegeman

UAI 2022poster

Set-valued prediction is a well-known concept in multi-class classification. When a classifier is uncertain about the class label for a test instance, it can predict a set of classes instead of a single class. In this paper, we focus on hierarchical multi-class classification problems, where valid s…

Cited by 4 Source ↗PDF ↗Scholar ↗