| Statistical Guarantees for Variational Autoencoders Using PAC-Bayesian Theory SD Mbacke, F Clerc, P Germain Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| A general framework for the practical disintegration of PAC-Bayesian bounds P Viallard, P Germain, A Habrard, E Morvant Machine Learning 113 (2), 519-604, 2024 | 23 | 2024 |
| Interpretability in Machine Learning: on the Interplay with Explainability, Predictive Performances and Models B Leblanc, P Germain arXiv preprint arXiv:2311.11491, 2023 | 1 | 2023 |
| Sample Boosting Algorithm (SamBA)-An interpretable greedy ensemble classifier based on local expertise for fat data B Bauvin, C Capponi, F Clerc, P Germain, S Koço, J Corbeil Uncertainty in Artificial Intelligence, 130-140, 2023 | | 2023 |
| Invariant Causal Set Covering Machines T Godon, B Bauvin, P Germain, J Corbeil, A Drouin arXiv preprint arXiv:2306.04777, 2023 | | 2023 |
| PAC-Bayesian Generalization Bounds for Adversarial Generative Models S Diarra Mbacke, F Clerc, P Germain arXiv e-prints, arXiv: 2302.08942, 2023 | 7* | 2023 |
| Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm LP Vignault, A Durand, P Germain Journal of Machine Learning Research 24 (294), 1-13, 2023 | | 2023 |
| Interpretable domain adaptation using unsupervised feature selection on pre-trained source models L Zhang, P Germain, Y Kessaci, C Biernacki Neurocomputing 511, 319-336, 2022 | 2 | 2022 |
| Seeking Interpretability and Explainability in Binary Activated Neural Networks B Leblanc, P Germain arXiv preprint arXiv:2209.03450, 2022 | | 2022 |
| Interpretable domain adaptation for hidden subdomain alignment in the context of pre-trained source models L Zhang, P Germain, Y Kessaci, C Biernacki Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 9057-9065, 2022 | 4 | 2022 |
| A Greedy Algorithm for Building Compact Binary Activated Neural Networks. B Leblanc, P Germain CoRR abs/2209.03450, 2022 | 1 | 2022 |
| Learning stochastic majority votes by minimizing a PAC-Bayes generalization bound V Zantedeschi, P Viallard, E Morvant, R Emonet, A Habrard, P Germain, ... Advances in Neural Information Processing Systems 34, 455-467, 2021 | 17 | 2021 |
| PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations L Fortier-Dubois, B Leblanc, G Letarte, F Laviolette, P Germain arXiv preprint arXiv:2110.15137, 2021 | | 2021 |
| Apprentissage de Vote de Majorité par Minimisation d'une C-Borne PAC-Bayésienne P Viallard, P Germain, E Morvant CAp 2021, 2021 | | 2021 |
| Learning aggregations of binary activated neural networks with probabilities over representations L Fortier-Dubois, G Letarte, B Leblanc, F Laviolette, P Germain CoRR, abs/2110.15137, 2021 | 2 | 2021 |
| Self-bounding majority vote learning algorithms by the direct minimization of a tight PAC-Bayesian C-bound P Viallard, P Germain, A Habrard, E Morvant Machine Learning and Knowledge Discovery in Databases. Research Track …, 2021 | 8 | 2021 |
| Landmark-based ensemble learning with random Fourier features and gradient boosting L Gautheron, P Germain, A Habrard, G Metzler, E Morvant, M Sebban, ... Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021 | 5 | 2021 |
| Out-of-distribution detection for regression tasks: parameter versus predictor entropy Y Pequignot, M Alain, P Dallaire, A Yeganehparast, P Germain, ... arXiv preprint arXiv:2010.12995, 2020 | 1* | 2020 |
| Target to source coordinate-wise adaptation of pre-trained models L Zhang, P Germain, Y Kessaci, C Biernacki Joint European Conference on Machine Learning and Knowledge Discovery in …, 2020 | 3 | 2020 |
| PAC-Bayesian contrastive unsupervised representation learning K Nozawa, P Germain, B Guedj Conference on Uncertainty in Artificial Intelligence, 21-30, 2020 | 24 | 2020 |
Emilie MorvantAssistant Professor, University of Saint-Etienne (France), Hubert Curien LaboratoryVerified email at univ-st-etienne.fr
