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
30012016
Domain-adversarial neural networks
H Ajakan, P Germain, H Larochelle, F Laviolette, M Marchand
arXiv preprint arXiv:1412.4446, 2014
2082014
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
1802009
PAC-Bayesian theory meets Bayesian inference
P Germain, F Bach, A Lacoste, S Lacoste-Julien
Advances in Neural Information Processing Systems, 1884-1892, 2016
902016
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
892015
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
872007
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
862013
PAC-Bayesian bounds based on the Rényi divergence
L Bégin, P Germain, F Laviolette, JF Roy
Artificial Intelligence and Statistics, 435-444, 2016
422016
A new PAC-Bayesian perspective on domain adaptation
P Germain, A Habrard, F Laviolette, E Morvant
International conference on machine learning, 859-868, 2016
392016
PAC-Bayesian Theory for Transductive Learning
L Bégin, P Germain, F Laviolette, JF Roy
Proceedings of the Seventeenth International Conference on Artificial …, 2014
262014
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
262009
A PAC-Bayes sample-compression approach to kernel methods
P Germain, A Lacoste, F Laviolette, M Marchand, S Shanian
ICML, 2011
252011
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
202007
Dichotomize and generalize: PAC-Bayesian binary activated deep neural networks
G Letarte, P Germain, B Guedj, F Laviolette
arXiv preprint arXiv:1905.10259, 2019
92019
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-Bayes and domain adaptation
P Germain, A Habrard, F Laviolette, E Morvant
Neurocomputing 379, 379-397, 2020
62020
Improved PAC-Bayesian Bounds for Linear Regression
V Shalaeva, AF Esfahani, P Germain, M Petreczky
AAAI, 5660-5667, 2020
52020
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
52012
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
42019
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
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