| Domain-adversarial training of neural networks Y Ganin, E Ustinova, H Ajakan, P Germain, H Larochelle, F Laviolette, ... Journal of machine learning research 17 (59), 1-35, 2016 | 8868 | 2016 |
| Domain-adversarial neural networks H Ajakan, P Germain, H Larochelle, F Laviolette, M Marchand arXiv preprint arXiv:1412.4446, 2014 | 391 | 2014 |
| PAC-Bayesian Learning of Linear Classifiers P Germain, A Lacasse, F Laviolette, M Marchand Proceedings of the 26th Annual International Conference on Machine Learning …, 2009 | 281 | 2009 |
| PAC-Bayesian theory meets Bayesian inference P Germain, F Bach, A Lacoste, S Lacoste-Julien Advances in Neural Information Processing Systems, 1884-1892, 2016 | 193 | 2016 |
| Risk bounds for the majority vote: From a PAC-Bayesian analysis to a learning algorithm P Germain, A Lacasse, F Laviolette, M Marchand, JF Roy arXiv preprint arXiv:1503.08329, 2015 | 156 | 2015 |
| A PAC-Bayesian approach for domain adaptation with specialization to linear classifiers P Germain, A Habrard, F Laviolette, E Morvant International conference on machine learning, 738-746, 2013 | 140 | 2013 |
| PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier A Lacasse, F Laviolette, M Marchand, P Germain, N Usunier | 122 | 2007 |
| PAC-Bayesian bounds based on the Rényi divergence L Bégin, P Germain, F Laviolette, JF Roy Artificial Intelligence and Statistics, 435-444, 2016 | 103 | 2016 |
| A new PAC-Bayesian perspective on domain adaptation P Germain, A Habrard, F Laviolette, E Morvant International conference on machine learning, 859-868, 2016 | 81 | 2016 |
| Dichotomize and generalize: PAC-Bayesian binary activated deep neural networks G Letarte, P Germain, B Guedj, F Laviolette Advances in Neural Information Processing Systems 32, 2019 | 60 | 2019 |
| PAC-Bayesian Theory for Transductive Learning L Bégin, P Germain, F Laviolette, JF Roy Proceedings of the Seventeenth International Conference on Artificial …, 2014 | 51 | 2014 |
| PAC-Bayes and domain adaptation P Germain, A Habrard, F Laviolette, E Morvant Neurocomputing 379, 379-397, 2020 | 33 | 2020 |
| A PAC-Bayes sample compression approach to kernel methods P Germain, A Lacoste, F Laviolette, M Marchand, S Shanian Proceedings of the 28th International Conference on International Conference …, 2011 | 30 | 2011 |
| From PAC-Bayes bounds to KL regularization P Germain, A Lacasse, F Laviolette, M Marchand, S Shanian Advances in Neural Information Processing Systems 22, 603-610, 2009 | 29 | 2009 |
| 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 |
| PAC-Bayesian contrastive unsupervised representation learning K Nozawa, P Germain, B Guedj Conference on Uncertainty in Artificial Intelligence, 21-30, 2020 | 23 | 2020 |
| PAC-Bayesian analysis for a two-step hierarchical multiview learning approach A Goyal, E Morvant, P Germain, MR Amini Machine Learning and Knowledge Discovery in Databases: European Conference …, 2017 | 23 | 2017 |
| A PAC-Bayes Risk Bound for General Loss Functions P Germain, A Lacasse, F Laviolette, M Marchand Advances in neural information processing systems 19, 449, 2007 | 19 | 2007 |
| 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 |
| Improved PAC-Bayesian Bounds for Linear Regression V Shalaeva, AF Esfahani, P Germain, M Petreczky AAAI, 5660-5667, 2020 | 16 | 2020 |
Emilie MorvantAssistant Professor, University of Saint-Etienne (France), Hubert Curien Laboratory在 univ-st-etienne.fr 的电子邮件经过验证
