Sébastien Destercke — accepted AI-conference papers

2026

A Survey on Quantitative Possibility Theory in Artificial Intelligence. A Convenient Uncertainty and Preference Model

Henri Prade, Sébastien Destercke, Didier Dubois

IJCAI 2026

Quantitative (or numerical) possibility theory offers a simple but yet very expressive setting for handling higher-order uncertainty and in particular imprecise probabilities. The paper surveys the basic ideas and notions underlying numerical possibility theory, its relation to the other uncertainty

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2022

Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison

Eyke Hüllermeier, Sébastien Destercke, Mohammad Hossein Shaker

UAI 2022poster

The representation and quantification of uncertainty has received increasing attention in machine learning in the recent past. The formalism of credal sets provides an interesting alternative in this regard, especially as it combines the representation of epistemic (lack of knowledge) and aleatoric…

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