4 papers
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…
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…
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…
Real bird dataset with imprecise and uncertain values
Constance Thierry, Arthur Hoarau, Arnaud Martin +2
The theory of belief functions allows the fusion of imperfect data from different sources. Unfortunately, few real, imprecise and uncertain datasets exist to test approaches using…