3 papers
cs.LG2026
MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness
Jeanne Monnier, Thomas George, Frédéric Guyard +2
Fairness in machine learning remains challenging due to its ethical complexity, the absence of a universal definition, and the need for context-specific bias metrics. Existing meth…
cs.LG2025
Calibration improves detection of mislabeled examples
Ilies Chibane, Thomas George, Pierre Nodet +1
Mislabeled data is a pervasive issue that undermines the performance of machine learning systems in real-world applications. An effective approach to mitigate this problem is to de…
cs.LG2024
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…