| From Weakly Supervised Learning to Biquality Learning: an Introduction P Nodet, V Lemaire, A Bondu, A Cornuéjols, A Ouorou International Joint Conference on Neural Networks (IJCNN), 2021 | 38 | 2021 |
| Importance Reweighting for Biquality Learning P Nodet, V Lemaire, A Bondu, A Cornuéjols, A Ouorou International Joint Conference on Neural Networks (IJCNN), 2021 | 8 | 2021 |
| Contrastive representations for label noise require fine-tuning P Nodet, V Lemaire, A Bondu, A Cornuéjols ECML/PKDD (Interactive Adaptive Learning Workshop), 2021 | 2 | 2021 |
| Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark T George, P Nodet, A Bondu, V Lemaire Transactions on Machine Learning Research (TMLR), 2024 | 1 | 2024 |
| biquality-learn: a Python library for Biquality Learning P Nodet, V Lemaire, A Bondu, A Cornuéjols arXiv preprint arXiv:2308.09643, 2023 | | 2023 |
| Biquality learning: from weakly supervised learning to distribution shifts P Nodet Université Paris-Saclay, 2023 | | 2023 |
| Biquality learning: a framework to design algorithms dealing with closed-set distribution shifts P Nodet, V Lemaire, A Bondu, A Cornuéjols Machine Learning, 2023 | | 2023 |
| Repondération Préférentielle pour l’Apprentissage Biqualité P Nodet, V Lemaire, A Bondu, A Cornuéjols Extraction et Gestion des Connaissance (EGC), 2022 | | 2022 |
| La détection d’exemples mal-étiquetés vue comme l’introspection de modèles d’apprentissage: concepts, recensement et étude comparative T George, P Nodet, A Bondu, V Lemaire | | |
Vincent LemaireOrange LabsVerified email at orange.com
