Pascal Germain

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 46

Top Topics

Learning Algorithm

Bayesian Analysis

Domain Adaptation

Top Venues

Publications

Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant
A general framework for the practical disintegration of PAC-Bayesian bounds. Mach. Learn. 113 (2) (2024)
Shubham Gupta, Mirco Ravanelli, Pascal Germain, Cem Subakan
Phoneme Discretized Saliency Maps for Explainable Detection of AI-Generated Voice. CoRR (2024)
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)
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)
Thibaud Godon, Baptiste Bauvin, Pascal Germain, Jacques Corbeil, Alexandre Drouin
Invariant Causal Set Covering Machines. CoRR (2023)
Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory. CoRR (2023)
Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain
PAC-Bayesian Generalization Bounds for Adversarial Generative Models. CoRR (2023)
Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain
PAC-Bayesian Generalization Bounds for Adversarial Generative Models. ICML (2023)
Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory. NeurIPS (2023)
Benjamin Leblanc, Pascal Germain
Interpretability in Machine Learning: on the Interplay with Explainability, Predictive Performances and Models. CoRR (2023)
Luxin Zhang, Pascal Germain, Yacine Kessaci, Christophe Biernacki
Interpretable domain adaptation using unsupervised feature selection on pre-trained source models. Neurocomputing 511 (2022)
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)
Benjamin Leblanc, Pascal Germain
A Greedy Algorithm for Building Compact Binary Activated Neural Networks. CoRR (2022)
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)
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)
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 (2021)
Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant
A General Framework for the Derandomization of PAC-Bayesian Bounds. CoRR (2021)
Louis Fortier-Dubois, Gaël Letarte, Benjamin Leblanc, François Laviolette, Pascal Germain
Learning Aggregations of Binary Activated Neural Networks with Probabilities over Representations. CoRR (2021)
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 (2021)
Vera Shalaeva, Alireza Fakhrizadeh Esfahani, Pascal Germain, Mihály Petreczky
Improved PAC-Bayesian Bounds for Linear Regression. AAAI (2020)
Kento Nozawa, Pascal Germain, Benjamin Guedj
PAC-Bayesian Contrastive Unsupervised Representation Learning. UAI (2020)
Luxin Zhang, Pascal Germain, Yacine Kessaci, Christophe Biernacki
Target to Source Coordinate-Wise Adaptation of Pre-trained Models. ECML/PKDD (1) (2020)
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)
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
PAC-Bayes and domain adaptation. Neurocomputing 379 (2020)
Yann Pequignot, Mathieu Alain, Patrick Dallaire, Alireza Yeganehparast, Pascal Germain, Josée Desharnais, François Laviolette
Implicit Variational Inference: the Parameter and the Predictor Space. CoRR (2020)
Kento Nozawa, Pascal Germain, Benjamin Guedj
PAC-Bayesian Contrastive Unsupervised Representation Learning. CoRR (2019)
Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters. Neurocomputing 358 (2019)
Gaël Letarte, Emilie Morvant, Pascal Germain
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior. AISTATS (2019)
Vera Shalaeva, Alireza Fakhrizadeh Esfahani, Pascal Germain, Mihály Petreczky
Improved PAC-Bayesian Bounds for Linear Regression. CoRR (2019)
Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. CoRR (2019)
Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, Valentina Zantedeschi
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting. CoRR (2019)
Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. NeurIPS (2019)
Gaël Letarte, Emilie Morvant, Pascal Germain
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior. CoRR (2018)
Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters. CoRR (2018)
Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini
PAC-Bayesian Analysis for a Two-Step Hierarchical Multiview Learning Approach. ECML/PKDD (2) (2017)
Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy
PAC-Bayesian Bounds based on the Rényi Divergence. AISTATS (2016)
Pascal Germain, Francis R. Bach, Alexandre Lacoste, Simon Lacoste-Julien
PAC-Bayesian Theory Meets Bayesian Inference. CoRR (2016)
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
A New PAC-Bayesian Perspective on Domain Adaptation. ICML (2016)
Pascal Germain, Francis R. Bach, Alexandre Lacoste, Simon Lacoste-Julien
PAC-Bayesian Theory Meets Bayesian Inference. NIPS (2016)
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 (2016)
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
A New PAC-Bayesian Perspective on Domain Adaptation. CoRR (2015)
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers. CoRR (2015)
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context. CoRR (2015)
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 (2015)
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 (2015)
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky
Domain-Adversarial Training of Neural Networks. CoRR (2015)
Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand
Domain-Adversarial Neural Networks. CoRR (2014)
Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy
PAC-Bayesian Theory for Transductive Learning. AISTATS (2014)
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers. ICML (3) (2013)
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
PAC-Bayesian Learning and Domain Adaptation CoRR (2012)
Pascal Germain, Sébastien Giguère, Jean-Francis Roy, Brice Zirakiza, François Laviolette, Claude-Guy Quimper
A Pseudo-Boolean Set Covering Machine. CP (2012)
Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian
A PAC-Bayes Sample-compression Approach to Kernel Methods. ICML (2011)
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand, Sara Shanian
From PAC-Bayes Bounds to KL Regularization. NIPS (2009)
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand
PAC-Bayesian learning of linear classifiers. ICML (2009)
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)
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand
A PAC-Bayes Risk Bound for General Loss Functions. NIPS (2006)