A PAC-Bayes Risk Bound for General Loss Functions. - Pascal Germain; Alexandre Lacasse; François Laviolette; Mario Marchand (NIPS, 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 (NIPS, 2006)

PAC-Bayesian learning of linear classifiers. - Pascal Germain; Alexandre Lacasse; François Laviolette; Mario Marchand (ICML, 2009)

From PAC-Bayes Bounds to KL Regularization. - Pascal Germain; Alexandre Lacasse; François Laviolette; Mario Marchand; Sara Shanian (NIPS, 2009)

A PAC-Bayes Sample-compression Approach to Kernel Methods. - Pascal Germain; Alexandre Lacoste; François Laviolette; Mario Marchand; Sara Shanian (ICML, 2011)

A Pseudo-Boolean Set Covering Machine. - Pascal Germain; Sébastien Giguère; Jean-Francis Roy; Brice Zirakiza; François Laviolette; Claude-Guy Quimper (CP, 2012)

PAC-Bayesian Learning and Domain Adaptation - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (CoRR, 2012)

A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (ICML (3), 2013)

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

Domain-Adversarial Neural Networks. - Hana Ajakan; Pascal Germain; Hugo Larochelle; François Laviolette; Mario Marchand (CoRR, 2014)

An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (CoRR, 2015)

PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (CoRR, 2015)

Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm. - Pascal Germain; Alexandre Lacasse; François Laviolette; Mario Marchand; Jean-Francis Roy (J. Mach. Learn. Res., 2015)

PAC-Bayesian Bounds based on the Rényi Divergence. - Luc Bégin; Pascal Germain; François Laviolette; Jean-Francis Roy (AISTATS, 2016)

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

PAC-Bayesian Theory Meets Bayesian Inference. - Pascal Germain; Francis R. Bach; Alexandre Lacoste; Simon Lacoste-Julien (NIPS, 2016)

Domain-Adversarial Training of Neural Networks. - Yaroslav Ganin; Evgeniya Ustinova; Hana Ajakan; Pascal Germain; Hugo Larochelle; François Laviolette; Mario Marchand; Victor S. Lempitsky (J. Mach. Learn. Res., 2016)

PAC-Bayesian Analysis for a Two-Step Hierarchical Multiview Learning Approach. - Anil Goyal; Emilie Morvant; Pascal Germain; Massih-Reza Amini (ECML/PKDD (2), 2017)

Domain-Adversarial Training of Neural Networks. - Yaroslav Ganin; Evgeniya Ustinova; Hana Ajakan; Pascal Germain; Hugo Larochelle; François Laviolette; Mario Marchand; Victor S. Lempitsky (Domain Adaptation in Computer Vision Applications, 2017)

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

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks. - Gaël Letarte; Pascal Germain; Benjamin Guedj; François Laviolette (NeurIPS, 2019)

Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting. - Léo Gautheron; Pascal Germain; Amaury Habrard; Emilie Morvant; Marc Sebban; Valentina Zantedeschi (CoRR, 2019)

Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters. - Anil Goyal; Emilie Morvant; Pascal Germain; Massih-Reza Amini (Neurocomputing, 2019)

Improved PAC-Bayesian Bounds for Linear Regression. - Vera Shalaeva; Alireza Fakhrizadeh Esfahani; Pascal Germain; Mihály Petreczky (AAAI, 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 (ECML/PKDD (3), 2020)

Target to Source Coordinate-Wise Adaptation of Pre-trained Models. - Luxin Zhang; Pascal Germain; Yacine Kessaci; Christophe Biernacki (ECML/PKDD (1), 2020)

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

Implicit Variational Inference: the Parameter and the Predictor Space. - Yann Pequignot; Mathieu Alain; Patrick Dallaire; Alireza Yeganehparast; Pascal Germain; Josée Desharnais; François Laviolette (CoRR, 2020)

PAC-Bayes and domain adaptation. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (Neurocomputing, 2020)

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 (NeurIPS, 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 (ECML/PKDD (2), 2021)

A General Framework for the Derandomization of PAC-Bayesian Bounds. - Paul Viallard; Pascal Germain; Amaury Habrard; Emilie Morvant (CoRR, 2021)

Learning Aggregations of Binary Activated Neural Networks with Probabilities over Representations. - Louis Fortier-Dubois; Gaël Letarte; Benjamin Leblanc; François Laviolette; Pascal Germain (CoRR, 2021)

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

A Greedy Algorithm for Building Compact Binary Activated Neural Networks. - Benjamin Leblanc; Pascal Germain (CoRR, 2022)

Interpretable domain adaptation using unsupervised feature selection on pre-trained source models. - Luxin Zhang; Pascal Germain; Yacine Kessaci; Christophe Biernacki (Neurocomputing, 2022)

PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations. - Louis Fortier-Dubois; Benjamin Leblanc; Gaël Letarte; François Laviolette; Pascal Germain (Canadian AI, 2023)

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

Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory. - Sokhna Diarra Mbacke; Florence Clerc; Pascal Germain (NeurIPS, 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 (UAI, 2023)

Invariant Causal Set Covering Machines. - Thibaud Godon; Baptiste Bauvin; Pascal Germain; Jacques Corbeil; Alexandre Drouin (CoRR, 2023)

Interpretability in Machine Learning: on the Interplay with Explainability, Predictive Performances and Models. - Benjamin Leblanc; Pascal Germain (CoRR, 2023)

Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm. - Louis-Philippe Vignault; Audrey Durand; Pascal Germain (J. Mach. Learn. Res., 2023)

Phoneme Discretized Saliency Maps for Explainable Detection of AI-Generated Voice. - Shubham Gupta; Mirco Ravanelli; Pascal Germain; Cem Subakan (INTERSPEECH, 2024)

Seeking Interpretability and Explainability in Binary Activated Neural Networks. - Benjamin Leblanc; Pascal Germain (xAI (1), 2024)

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

A general framework for the practical disintegration of PAC-Bayesian bounds. - Paul Viallard; Pascal Germain; Amaury Habrard; Emilie Morvant (Mach. Learn., 2024)

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

Sample Compression for Continual Learning. - Jacob Comeau; Mathieu Bazinet; Pascal Germain; Cem Subakan (CoRR, 2025)