| Domain-adversarial training of neural networks Y Ganin, E Ustinova, H Ajakan, P Germain, H Larochelle, F Laviolette, ... The Journal of Machine Learning Research 17 (1), 2096-2030, 2016 | 1745 | 2016 |
| Domain-adversarial neural networks H Ajakan, P Germain, H Larochelle, F Laviolette, M Marchand arXiv preprint arXiv:1412.4446, 2014 | 153 | 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 | 148 | 2009 |
| 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 | 76 | 2007 |
| 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 | 73 | 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 | 68 | 2013 |
| PAC-Bayesian theory meets Bayesian inference P Germain, F Bach, A Lacoste, S Lacoste-Julien Advances in Neural Information Processing Systems, 1884-1892, 2016 | 57 | 2016 |
| PAC-Bayesian bounds based on the Rényi divergence L Bégin, P Germain, F Laviolette, JF Roy Artificial Intelligence and Statistics, 435-444, 2016 | 30 | 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 | 27 | 2016 |
| 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 | 24 | 2009 |
| A PAC-Bayes sample-compression approach to kernel methods P Germain, A Lacoste, F Laviolette, M Marchand, S Shanian ICML, 2011 | 23 | 2011 |
| PAC-Bayesian Theory for Transductive Learning L Bégin, P Germain, F Laviolette, JF Roy Proceedings of the Seventeenth International Conference on Artificial …, 2014 | 19 | 2014 |
| 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 | 17 | 2007 |
| PAC-Bayesian analysis for a two-step hierarchical multiview learning approach A Goyal, E Morvant, P Germain, MR Amini Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017 | 8 | 2017 |
| PAC-Bayesian theorems for domain adaptation with specialization to linear classifiers P Germain, A Habrard, F Laviolette, E Morvant arXiv preprint arXiv:1503.06944, 2015 | 4 | 2015 |
| A Pseudo-Boolean Set Covering Machine P Germain, S Giguere, JF Roy, B Zirakiza, F Laviolette, CG Quimper Principles and Practice of Constraint Programming, 916-924, 2012 | 4 | 2012 |
| Généralisations de la théorie PAC-bayésienne pour l’apprentissage inductif, l’apprentissage transductif et l’adaptation de domaine P Germain PhD thesis, Université Laval, 2015 | 3 | 2015 |
| PAC-Bayes and domain adaptation P Germain, A Habrard, F Laviolette, E Morvant Neurocomputing 379, 379-397, 2020 | 2 | 2020 |
| Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters A Goyal, E Morvant, P Germain, MR Amini Neurocomputing 358, 81-92, 2019 | 2 | 2019 |
| Dichotomize and generalize: Pac-bayesian binary activated deep neural networks G Letarte, P Germain, B Guedj, F Laviolette Advances in Neural Information Processing Systems, 6869-6879, 2019 | 1 | 2019 |
François LavioletteUniversité Laval, Associate member at MILAVerified email at ift.ulaval.ca
