| A PAC-Bayes Analysis of Adversarial Robustness P Viallard, G Vidot, A Habrard, E Morvant Advances in Neural Information Processing Systems 34, 2021 | 22 | 2021 |
| A General Framework for the Practical Disintegration of PAC-Bayesian Bounds P Viallard, P Germain, A Habrard, E Morvant arXiv preprint arXiv:2102.08649, 2021 | 18 | 2021 |
| 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 |
| 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 |
| Learning via Wasserstein-based high probability generalisation bounds P Viallard, M Haddouche, U Simsekli, B Guedj Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |
| A general framework for the practical disintegration of PAC-Bayesian bounds P Viallard, P Germain, A Habrard, E Morvant Machine Learning, 1-86, 2023 | 5 | 2023 |
| Interpreting neural networks as majority votes through the PAC-Bayesian theory P Viallard, R Emonet, P Germain, A Habrard, E Morvant Workshop on Machine Learning with guarantees@ NeurIPS 2019, 2019 | 5 | 2019 |
| Tighter Generalisation Bounds via Interpolation P Viallard, M Haddouche, U Şimşekli, B Guedj arXiv preprint arXiv:2402.05101, 2024 | 2 | 2024 |
| Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures P Viallard, R Emonet, A Habrard, E Morvant, V Zantedeschi arXiv preprint arXiv:2402.13285, 2024 | | 2024 |
| A PAC-Bayesian Link Between Generalisation and Flat Minima M Haddouche, P Viallard, U Simsekli, B Guedj arXiv preprint arXiv:2402.08508, 2024 | | 2024 |
| From Mutual Information to Expected Dynamics: New Generalization Bounds for Heavy-Tailed SGD B Dupuis, P Viallard NeurIPS 2023 Workshop Heavy Tails in Machine Learning, 2023 | | 2023 |
| Bornes de généralisation: quand l'information mutuelle rencontre les bornes PAC-Bayésiennes et désintégrées P Viallard CAp 2023, 2023 | | 2023 |
| PAC-Bayesian Bounds and Beyond: Self-Bounding Algorithms and New Perspectives on Generalization in Machine Learning P Viallard Université Jean Monnet-Saint-Etienne, 2022 | | 2022 |
| Intérêt des bornes désintégrées pour la généralisation avec des mesures de complexité P Viallard, R Emonet, P Germain, A Habrard, E Morvant, V Zantedeschi CAp 2022, 2022 | | 2022 |
| Dérandomisation des Bornes PAC-Bayésiennes P Viallard, P Germain, E Morvant CAp 2021, 2021 | | 2021 |
| Une Analyse PAC-Bayésienne de la Robustesse Adversariale G Vidot, P Viallard, E Morvant Conférence sur l'Apprentissage automatique (CAp 2021), 2021 | | 2021 |
Emilie MorvantAssistant Professor, University of Saint-Etienne (France), Hubert Curien LaboratoryVerified email at univ-st-etienne.fr
