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ORCIDPascal Germain
Person information
- affiliation: Inria Lille, France
- affiliation (former): Laval University, Canada
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Journal Articles
- 2024
[j7]Paul Viallard
, Pascal Germain, Amaury Habrard, Emilie Morvant:
A general framework for the practical disintegration of PAC-Bayesian bounds. Mach. Learn. 113(2): 519-604 (2024)- 2023
- [j6]Louis-Philippe Vignault, Audrey Durand, Pascal Germain:
Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm. J. Mach. Learn. Res. 24: 294:1-294:13 (2023) - 2022
- [j5]Luxin Zhang, Pascal Germain, Yacine Kessaci, Christophe Biernacki:
Interpretable domain adaptation using unsupervised feature selection on pre-trained source models. Neurocomputing 511: 319-336 (2022) - 2020
- [j4]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
PAC-Bayes and domain adaptation. Neurocomputing 379: 379-397 (2020) - 2019
- [j3]Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini:
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters. Neurocomputing 358: 81-92 (2019) - 2016
- [j2]Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky:
Domain-Adversarial Training of Neural Networks. J. Mach. Learn. Res. 17: 59:1-59:35 (2016) - 2015
- [j1]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Jean-Francis Roy:
Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm. J. Mach. Learn. Res. 16: 787-860 (2015)
Conference and Workshop Papers
- 2024
- [c26]Benjamin Leblanc, Pascal Germain:
Seeking Interpretability and Explainability in Binary Activated Neural Networks. xAI (1) 2024: 3-20 - 2023
- [c25]Louis Fortier-Dubois, Benjamin Leblanc, Gaël Letarte, François Laviolette, Pascal Germain:
PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations. Canadian AI 2023 - [c24]Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain:
PAC-Bayesian Generalization Bounds for Adversarial Generative Models. ICML 2023: 24271-24290 - [c23]Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain:
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory. NeurIPS 2023 - [c22]Baptiste Bauvin, Cécile Capponi, Florence Clerc, Pascal Germain, Sokol Koço, Jacques Corbeil:
Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat data. UAI 2023: 130-140 - 2022
- [c21]Luxin Zhang, Pascal Germain, Yacine Kessaci, Christophe Biernacki:
Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-trained Source Models. AAAI 2022: 9057-9065 - 2021
- [c20]Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj:
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. NeurIPS 2021: 455-467 - [c19]Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant:
Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound. ECML/PKDD (2) 2021: 167-183 - 2020
- [c18]Vera Shalaeva, Alireza Fakhrizadeh Esfahani, Pascal Germain, Mihály Petreczky:
Improved PAC-Bayesian Bounds for Linear Regression. AAAI 2020: 5660-5667 - [c17]Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban, Valentina Zantedeschi:
Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting. ECML/PKDD (3) 2020: 141-157 - [c16]Luxin Zhang, Pascal Germain, Yacine Kessaci, Christophe Biernacki:
Target to Source Coordinate-Wise Adaptation of Pre-trained Models. ECML/PKDD (1) 2020: 378-394 - [c15]Kento Nozawa, Pascal Germain, Benjamin Guedj:
PAC-Bayesian Contrastive Unsupervised Representation Learning. UAI 2020: 21-30 - 2019
- [c14]Gaël Letarte, Emilie Morvant, Pascal Germain:
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior. AISTATS 2019: 768-776 - [c13]Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette:
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. NeurIPS 2019: 6869-6879 - 2017
- [c12]Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini:
PAC-Bayesian Analysis for a Two-Step Hierarchical Multiview Learning Approach. ECML/PKDD (2) 2017: 205-221 - 2016
- [c11]Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy:
PAC-Bayesian Bounds based on the Rényi Divergence. AISTATS 2016: 435-444 - [c10]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
A New PAC-Bayesian Perspective on Domain Adaptation. ICML 2016: 859-868 - [c9]Pascal Germain, Francis R. Bach, Alexandre Lacoste, Simon Lacoste-Julien:
PAC-Bayesian Theory Meets Bayesian Inference. NIPS 2016: 1876-1884 - 2014
- [c8]Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy:
PAC-Bayesian Theory for Transductive Learning. AISTATS 2014: 105-113 - 2013
- [c7]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers. ICML (3) 2013: 738-746 - 2012
- [c6]Pascal Germain, Sébastien Giguère, Jean-Francis Roy, Brice Zirakiza, François Laviolette, Claude-Guy Quimper:
A Pseudo-Boolean Set Covering Machine. CP 2012: 916-924 - 2011
- [c5]Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian:
A PAC-Bayes Sample-compression Approach to Kernel Methods. ICML 2011: 297-304 - 2009
- [c4]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand:
PAC-Bayesian learning of linear classifiers. ICML 2009: 353-360 - [c3]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Sara Shanian:
From PAC-Bayes Bounds to KL Regularization. NIPS 2009: 603-610 - 2006
- [c2]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand:
A PAC-Bayes Risk Bound for General Loss Functions. NIPS 2006: 449-456 - [c1]Alexandre Lacasse, François Laviolette, Mario Marchand, Pascal Germain, Nicolas Usunier:
PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier. NIPS 2006: 769-776
Parts in Books or Collections
- 2017
- [p1]Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky:
Domain-Adversarial Training of Neural Networks. Domain Adaptation in Computer Vision Applications 2017: 189-209
Informal and Other Publications
- 2024
- [i26]Shubham Gupta, Mirco Ravanelli, Pascal Germain, Cem Subakan:
Phoneme Discretized Saliency Maps for Explainable Detection of AI-Generated Voice. CoRR abs/2406.10422 (2024) - [i25]Benjamin Leblanc, Mathieu Bazinet, Nathaniel D'Amours, Alexandre Drouin, Pascal Germain:
Sample Compression Hypernetworks: From Generalization Bounds to Meta-Learning. CoRR abs/2410.13577 (2024) - 2023
- [i24]Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain:
PAC-Bayesian Generalization Bounds for Adversarial Generative Models. CoRR abs/2302.08942 (2023) - [i23]Thibaud Godon, Baptiste Bauvin, Pascal Germain, Jacques Corbeil, Alexandre Drouin:
Invariant Causal Set Covering Machines. CoRR abs/2306.04777 (2023) - [i22]Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain:
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory. CoRR abs/2310.04935 (2023) - [i21]Benjamin Leblanc, Pascal Germain:
Interpretability in Machine Learning: on the Interplay with Explainability, Predictive Performances and Models. CoRR abs/2311.11491 (2023) - 2022
- [i20]Benjamin Leblanc, Pascal Germain:
A Greedy Algorithm for Building Compact Binary Activated Neural Networks. CoRR abs/2209.03450 (2022) - 2021
- [i19]Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant:
A General Framework for the Derandomization of PAC-Bayesian Bounds. CoRR abs/2102.08649 (2021) - [i18]Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant:
Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound. CoRR abs/2104.13626 (2021) - [i17]Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj:
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. CoRR abs/2106.12535 (2021) - [i16]Louis Fortier-Dubois, Gaël Letarte, Benjamin Leblanc, François Laviolette, Pascal Germain:
Learning Aggregations of Binary Activated Neural Networks with Probabilities over Representations. CoRR abs/2110.15137 (2021) - 2020
- [i15]Yann Pequignot, Mathieu Alain, Patrick Dallaire, Alireza Yeganehparast, Pascal Germain, Josée Desharnais, François Laviolette:
Implicit Variational Inference: the Parameter and the Predictor Space. CoRR abs/2010.12995 (2020) - 2019
- [i14]Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette:
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. CoRR abs/1905.10259 (2019) - [i13]Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, Valentina Zantedeschi:
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting. CoRR abs/1906.06203 (2019) - [i12]Kento Nozawa, Pascal Germain, Benjamin Guedj:
PAC-Bayesian Contrastive Unsupervised Representation Learning. CoRR abs/1910.04464 (2019) - [i11]Vera Shalaeva, Alireza Fakhrizadeh Esfahani, Pascal Germain, Mihály Petreczky:
Improved PAC-Bayesian Bounds for Linear Regression. CoRR abs/1912.03036 (2019) - 2018
- [i10]Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini:
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters. CoRR abs/1808.05784 (2018) - [i9]Gaël Letarte, Emilie Morvant, Pascal Germain:
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior. CoRR abs/1810.12683 (2018) - 2016
- [i8]Pascal Germain, Francis R. Bach, Alexandre Lacoste, Simon Lacoste-Julien:
PAC-Bayesian Theory Meets Bayesian Inference. CoRR abs/1605.08636 (2016) - 2015
- [i7]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context. CoRR abs/1501.03002 (2015) - [i6]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers. CoRR abs/1503.06944 (2015) - [i5]Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Jean-Francis Roy:
Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm. CoRR abs/1503.08329 (2015) - [i4]Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky:
Domain-Adversarial Training of Neural Networks. CoRR abs/1505.07818 (2015) - [i3]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
A New PAC-Bayesian Perspective on Domain Adaptation. CoRR abs/1506.04573 (2015) - 2014
- [i2]Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand:
Domain-Adversarial Neural Networks. CoRR abs/1412.4446 (2014) - 2012
- [i1]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
PAC-Bayesian Learning and Domain Adaptation. CoRR abs/1212.2340 (2012)
Coauthor Index
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