12 citations · 67 across the 26 of their papers we have counts for
27 papers
From Log-Odds to Shapley Values: An Explanatory Geometry for the Weighted Naive Bayes Classifier
Vincent Lemaire, Fabrice Clérot
This paper studies the construction of an explanatory space for a weighted naive Bayes classifier from the supervised representation induced by the model. We start from the classic…
Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark
Thomas George, Pierre Nodet, Alexis Bondu +1
Mislabeled examples are ubiquitous in real-world machine learning datasets, advocating the development of techniques for automatic detection. We show that most mislabeled detection…
A Practical Approach to Novel Class Discovery in Tabular Data
Colin Troisemaine, Alexandre Reiffers-Masson, Stéphane Gosselin +2
The problem of Novel Class Discovery (NCD) consists in extracting knowledge from a labeled set of known classes to accurately partition an unlabeled set of novel classes. While NCD…
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…
Viewing the process of generating counterfactuals as a source of knowledge: a new approach for explaining classifiers
Vincent Lemaire, Nathan Le Boudec, Victor Guyomard +1
There are now many explainable AI methods for understanding the decisions of a machine learning model. Among these are those based on counterfactual reasoning, which involve simula…
Biquality Learning: a Framework to Design Algorithms Dealing with Closed-Set Distribution Shifts
Pierre Nodet, Vincent Lemaire, Alexis Bondu +1
Training machine learning models from data with weak supervision and dataset shifts is still challenging. Designing algorithms when these two situations arise has not been explored…