Pascal Germain
Pascal Germain
Assistant Professor, Université Laval
Verified email at inria.fr - Homepage
TitleCited byYear
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
12452016
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
1442009
Domain-adversarial neural networks
H Ajakan, P Germain, H Larochelle, F Laviolette, M Marchand
arXiv preprint arXiv:1412.4446, 2014
1232014
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
692007
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
The Journal of Machine Learning Research 16 (1), 787-860, 2015
652015
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
622013
PAC-Bayesian theory meets Bayesian inference
P Germain, F Bach, A Lacoste, S Lacoste-Julien
Advances in Neural Information Processing Systems, 1884-1892, 2016
452016
PAC-Bayesian bounds based on the Rényi divergence
L Bégin, P Germain, F Laviolette, JF Roy
Artificial Intelligence and Statistics, 435-444, 2016
282016
A new PAC-Bayesian perspective on domain adaptation
P Germain, A Habrard, F Laviolette, E Morvant
International conference on machine learning, 859-868, 2016
262016
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
212011
PAC-Bayesian Theory for Transductive Learning
L Bégin, P Germain, F Laviolette, JF Roy
Proceedings of the Seventeenth International Conference on Artificial …, 2014
182014
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
72017
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
32015
Généralisations de la théorie PAC-bayésienne pour l'apprentissage inductif, l'apprentissage transductif et l'adaptation de domaine
P Germain
Université Laval, 2015
32015
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
32012
PAC-Bayes and domain adaptation
P Germain, A Habrard, F Laviolette, E Morvant
Neurocomputing, 2019
12019
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
G Letarte, P Germain, B Guedj, F Laviolette
arXiv preprint arXiv:1905.10259, 2019
12019
Pseudo-bayesian learning with kernel fourier transform as prior
G Letarte, E Morvant, P Germain
arXiv preprint arXiv:1810.12683, 2018
12018
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