Germain, Pascal

32 publications

ICML 2025 Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks Benjamin Leblanc, Mathieu Bazinet, Nathaniel D’Amours, Alexandre Drouin, Pascal Germain

AISTATS 2025 Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain

MLJ 2024 A General Framework for the Practical Disintegration of PAC-Bayesian Bounds Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant

NeurIPSW 2024 Sample Compression Hypernetworks: From Generalization Bounds to Meta-Learning Benjamin Leblanc, Mathieu Bazinet, Nathaniel D'Amours, Alexandre Drouin, Pascal Germain

NeurIPSW 2024 Sample Compression Unleashed : New Generalization Bounds for Real Valued Losses Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain

NeurIPSW 2024 Sample Compression Unleashed : New Generalization Bounds for Real Valued Losses Mathieu Bazinet, Valentina Zantedeschi, Pascal Germain

JMLR 2023 Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm Louis-Philippe Vignault, Audrey Durand, Pascal Germain

ICML 2023 PAC-Bayesian Generalization Bounds for Adversarial Generative Models Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain

UAI 2023 Sample Boosting Algorithm (SamBA) - An Interpretable Greedy Ensemble Classifier Based on Local Expertise for Fat Data Baptiste Bauvin, Cécile Capponi, Florence Clerc, Pascal Germain, Sokol Koço, Jacques Corbeil

NeurIPS 2023 Statistical Guarantees for Variational Autoencoders Using PAC-Bayesian Theory Sokhna Diarra Mbacke, Florence Clerc, Pascal Germain

AAAI 2022 Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-Trained Source Models Luxin Zhang, Pascal Germain, Yacine Kessaci, Christophe Biernacki

NeurIPS 2021 Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj

ECML-PKDD 2021 Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant

AAAI 2020 Improved PAC-Bayesian Bounds for Linear Regression Vera Shalaeva, Alireza Fakhrizadeh Esfahani, Pascal Germain, Mihály Petreczky

ECML-PKDD 2020 Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban, Valentina Zantedeschi

UAI 2020 PAC-Bayesian Contrastive Unsupervised Representation Learning Kento Nozawa, Pascal Germain, Benjamin Guedj

ECML-PKDD 2020 Target to Source Coordinate-Wise Adaptation of Pre-Trained Models Luxin Zhang, Pascal Germain, Yacine Kessaci, Christophe Biernacki

NeurIPS 2019 Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks Gaël Letarte, Pascal Germain, Benjamin Guedj, Francois Laviolette

AISTATS 2019 Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior Gaël Letarte, Emilie Morvant, Pascal Germain

ECML-PKDD 2017 PAC-Bayesian Analysis for a Two-Step Hierarchical Multiview Learning Approach Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini

ICML 2016 A New PAC-Bayesian Perspective on Domain Adaptation Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

JMLR 2016 Domain-Adversarial Training of Neural Networks Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario March, Victor Lempitsky

AISTATS 2016 PAC-Bayesian Bounds Based on the Rényi Divergence Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy

NeurIPS 2016 PAC-Bayesian Theory Meets Bayesian Inference Pascal Germain, Francis Bach, Alexandre Lacoste, Simon Lacoste-Julien

JMLR 2015 Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm Pascal Germain, Alexandre Lacasse, Francois Laviolette, Mario March, Jean-Francis Roy

AISTATS 2014 PAC-Bayesian Theory for Transductive Learning Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy

ICML 2013 A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

ICML 2011 A PAC-Bayes Sample-Compression Approach to Kernel Methods Pascal Germain, Alexandre Lacoste, François Laviolette, Mario Marchand, Sara Shanian

NeurIPS 2009 From PAC-Bayes Bounds to KL Regularization Pascal Germain, Alexandre Lacasse, Mario Marchand, Sara Shanian, François Laviolette

ICML 2009 PAC-Bayesian Learning of Linear Classifiers Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand

NeurIPS 2006 A PAC-Bayes Risk Bound for General Loss Functions Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand

NeurIPS 2006 PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier Alexandre Lacasse, François Laviolette, Mario Marchand, Pascal Germain, Nicolas Usunier