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Pascal Germain
Pascal Germain
Assistant Professor, Université Laval
Geverifieerd e-mailadres voor ift.ulaval.ca - Homepage
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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
103012016
Domain-adversarial neural networks
H Ajakan, P Germain, H Larochelle, F Laviolette, M Marchand
arXiv preprint arXiv:1412.4446, 2014
4292014
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
2912009
PAC-Bayesian theory meets Bayesian inference
P Germain, F Bach, A Lacoste, S Lacoste-Julien
Advances in Neural Information Processing Systems, 1884-1892, 2016
2112016
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
1712015
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
1492013
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
1272007
PAC-Bayesian bounds based on the Rényi divergence
L Bégin, P Germain, F Laviolette, JF Roy
Artificial Intelligence and Statistics, 435-444, 2016
1082016
A new PAC-Bayesian perspective on domain adaptation
P Germain, A Habrard, F Laviolette, E Morvant
International conference on machine learning, 859-868, 2016
852016
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
642019
PAC-Bayesian Theory for Transductive Learning
L Bégin, P Germain, F Laviolette, JF Roy
Proceedings of the Seventeenth International Conference on Artificial …, 2014
562014
PAC-Bayes and domain adaptation
P Germain, A Habrard, F Laviolette, E Morvant
Neurocomputing 379, 379-397, 2020
332020
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
322011
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
282009
PAC-Bayesian contrastive unsupervised representation learning
K Nozawa, P Germain, B Guedj
Conference on Uncertainty in Artificial Intelligence, 21-30, 2020
272020
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
262017
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
25*2024
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
192021
Improved PAC-Bayesian Bounds for Linear Regression
V Shalaeva, AF Esfahani, P Germain, M Petreczky
AAAI, 5660-5667, 2020
192020
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
192007
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Artikelen 1–20