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cs.LG2026
Can we trust our models? Epistemic calibration in second-order classification
Arthur Hoarau
Uncertainty estimation is critical for deploying machine learning models in high-stakes settings. However, classical calibration only assesses the reliability of predicted probabil…
cs.LG2024
DEMAU: Decompose, Explore, Model and Analyse Uncertainties
Arthur Hoarau, Vincent Lemaire
Recent research in machine learning has given rise to a flourishing literature on the quantification and decomposition of model uncertainty. This information can be very useful dur…
cs.LG2023
Evidential uncertainty sampling for active learning
Arthur Hoarau, Vincent Lemaire, Arnaud Martin +2
Recent studies in active learning, particularly in uncertainty sampling, have focused on the decomposition of model uncertainty into reducible and irreducible uncertainties. In thi…