Pascal Germain
Pascal Germain
Assistant Professor, Université Laval
Verified email at inria.fr - Homepage
Title
Cited by
Cited by
Year
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
17452016
Domain-adversarial neural networks
H Ajakan, P Germain, H Larochelle, F Laviolette, M Marchand
arXiv preprint arXiv:1412.4446, 2014
1532014
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
1482009
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
762007
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
732015
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
682013
PAC-Bayesian theory meets Bayesian inference
P Germain, F Bach, A Lacoste, S Lacoste-Julien
Advances in Neural Information Processing Systems, 1884-1892, 2016
572016
PAC-Bayesian bounds based on the Rényi divergence
L Bégin, P Germain, F Laviolette, JF Roy
Artificial Intelligence and Statistics, 435-444, 2016
302016
A new PAC-Bayesian perspective on domain adaptation
P Germain, A Habrard, F Laviolette, E Morvant
International conference on machine learning, 859-868, 2016
272016
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
242009
A PAC-Bayes sample-compression approach to kernel methods
P Germain, A Lacoste, F Laviolette, M Marchand, S Shanian
ICML, 2011
232011
PAC-Bayesian Theory for Transductive Learning
L Bégin, P Germain, F Laviolette, JF Roy
Proceedings of the Seventeenth International Conference on Artificial …, 2014
192014
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
172007
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
82017
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
42015
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
42012
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
32015
PAC-Bayes and domain adaptation
P Germain, A Habrard, F Laviolette, E Morvant
Neurocomputing 379, 379-397, 2020
22020
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
22019
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
12019
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