Imperfect Labels with Belief Functions for Active Learning. - Arthur Hoarau; Arnaud Martin 001; Jean-Christophe Dubois; Yolande Le Gall (BELIEF, 2022)

Real Bird Dataset with Imprecise and Uncertain Values. - Constance Thierry; Arthur Hoarau; Arnaud Martin 001; Jean-Christophe Dubois; Yolande Le Gall (BELIEF, 2022)

Datasets with Rich Labels for Machine Learning. - Arthur Hoarau; Constance Thierry; Arnaud Martin 001; Jean-Christophe Dubois; Yolande Le Gall (FUZZ, 2023)

Evidential uncertainties on rich labels for active learning. - Arthur Hoarau; Vincent Lemaire 001; Arnaud Martin 001; Jean-Christophe Dubois; Yolande Le Gall (CoRR, 2023)

Evidential Random Forests. - Arthur Hoarau; Arnaud Martin 001; Jean-Christophe Dubois; Yolande Le Gall (Expert Syst. Appl., 2023)

A Mean Distance Between Elements of Same Class for Rich Labels. - Arthur Hoarau; Constance Thierry; Jean-Christophe Dubois; Yolande Le Gall (BELIEF, 2024)

Méthode crédibiliste pour l'extraction d'incertitudes sans dépendance aux observations. - Arthur Hoarau; Vincent Lemaire 001; Arnaud Martin 001; Jean-Christophe Dubois; Yolande Le Gall (EGC, 2024)

DEMAU: Decompose, Explore, Model and Analyse Uncertainties. - Arthur Hoarau; Vincent Lemaire 001 (CoRR, 2024)

Apprentissage Actif de Données Incertaines et Imprécises. (Active Learning of Uncertain and Imprecise Data). - Arthur Hoarau (2024)

Evidential uncertainty sampling strategies for active learning. - Arthur Hoarau; Vincent Lemaire 001; Yolande Le Gall; Jean-Christophe Dubois; Arnaud Martin 001 (Mach. Learn., 2024)

DEMAU: Decompose, Explore, Model & Analyse Uncertainties. - Arthur Hoarau; Vincent Lemaire 001 (EGC, 2025)

Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition. - Arthur Hoarau; Benjamin Quost; Sébastien Destercke; Willem Waegeman (CoRR, 2025)

Robust Explanations Through Uncertainty Decomposition: A Path to Trustworthier AI. - Chenrui Zhu; Louenas Bounia; Vu-Linh Nguyen; Sébastien Destercke; Arthur Hoarau (CoRR, 2025)