Research Scientist in Machine Learning

MODAL Project-Team
Inria Lille - Nord Europe
40, avenue Halley
59650 Villeneuve d'Ascq, France
Laboratoire Paul Painlevé
Université de Lille
59655 Villeneuve d'Ascq, France

E-mail: firstname.lastname@inria.fr

Research interests: Learning theory (with an emphasis on PAC-Bayesian learning), domain adaptation, learning algorithms, kernel methods, ...

More about me (elsewhere) : Twitter, Linkedin, GitHub, Google Scholar, dblp, arXiv



News / Highlights


Students and Postdocs


Publications

Selected Reports
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural NetworksarXiv ]
Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette (2019)

PAC-Bayes and Domain AdaptationarXiv ]
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant (2017)

International Conference and Journal Papers
Pseudo-Bayesian Learning with Kernel Fourier Transform as Priorpdf, supplemental ] [ bibtex ] [ poster ] [ code, datasets ]
Gaël Letarte, Emilie Morvant, Pascal Germain (AISTATS 2019)

Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voterspublished version ] [ arXiv preprint ]
Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini (Neurocomputing 2019)

Domain-Adversarial Training of Neural Networkspdf ] [ bib ] [ source code: shallow version | deep version ] [ data ]
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor Lempitsky (JMLR 2016, and Springer 2017*)
*A slighlty shorter version of the JMLR version is published as a book chapter in Domain Adaptation in Computer Vision Applications (Editor: Gabriela Csurka).

PAC-Bayesian Analysis for a two-step Hierarchical Mutliview Learning Approachpdf ]
Anil Goyal, Emilie Morvant, Pascal Germain, Massih-Reza Amini (ECML 2017)

PAC-Bayesian Theory Meets Bayesian Inferencepaper ] [ spotlight: video | slides ] [ poster ] [ code ]
Pascal Germain, Francis Bach, Alexandre Lacoste, Simon Lacoste-Julien (NIPS 2016)

A New PAC-Bayesian Perspective on Domain Adaptationpdf ] [ supplemental ] [ bib ] [ source code ] [ data ]
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant (ICML 2016)

PAC-Bayesian Bounds based on the Rényi Divergencepaper ] [ bib ] [ poster ]
Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy (AISTATS 2016)

Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithmpdf ] [ bib ] [ source code ]
Pascal Germain, Alexandre Lacasse, François Laviolette, Mario Marchand and Jean-Francis Roy (JMLR 2015)

PAC-Bayesian Theory for Transductive Learningpaper, supplemental ] [ bib ] [ poster ] [ source code ]
Luc Bégin, Pascal Germain, François Laviolette, Jean-Francis Roy (AISTATS 2014)

A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifierspaper, supplemental ] [ bib ] [ source code ] [ data ] [ extended version ]
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant (ICML 2013)

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

A PAC-Bayes Sample Compression Approach to Kernel Methodspaper ] [ supplemental ]
Pascal Germain, Alexandre Lacoste, Francois Laviolette, Mario Marchand, and Sara Shanian (ICML 2011)

From PAC-Bayes Bounds to KL Regularizationpdf ]
Pascal Germain, Alexandre Lacasse, Francois Laviolette, Mario Marchand, and Sara Shanian (NIPS 2009)

PAC-Bayesian Learning of Linear Classifierpdf ]
Pascal Germain, Alexandre Lacasse, Francois Laviolette, and Mario Marchand (ICML 2009)

A PAC-Bayes Risk Bound for General Loss Functionspdf ]
Pascal Germain, Alexandre Lacasse, Francois Laviolette, and Mario Marchand (NIPS 2006)

PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifierpdf ]
Alexandre Lacasse, Francois Laviolette, Mario Marchand, Pascal Germain, and Nicolas Usunier (NIPS 2006)

Ph.D. Thesis
Généralisations de la théorie PAC-bayésienne pour l’apprentissage inductif, l’apprentissage transductif et l’adaptation de domainepdf (french) ] [ slides (french) ]
Pascal Germain (Université Laval, 2015)

Master's Thesis
Algorithmes d'apprentissage automatique inspirés de la théorie PAC-Bayespdf (french) ] [ bib ] english abstract ]
Pascal Germain (Université Laval, 2009)


Selected Talks

06/06/2019 : PAC-Bayesian Learning and Neural Networks; The Binary Activated Caseslides ]
51es Journées de Statistique (Nancy, France)

06/03/2019 : Réseau de neurones artificiels et apprentissage profondslides (french) ]
Journée de l'Enseignement de l'Informatique et de l'Algorithmique (Université de Lille, France)

24/01/2017 : Generalization of the PAC-Bayesian Theory, and Applications to Semi-Supervised Learningslides ]
Modal Seminar (INRIA Lille, France)

20/06/2016 : A New PAC-Bayesian Perspective on Domain Adaptationslides ]
ICML (New-York, US)

02/06/2016 : Variations on the PAC-Bayesian Boundslides ]
Bayes in Paris (École nationale de la statistique et de l'administration économique - ENSAE, Paris, France)

31/03/2016 : A Representation Learning Approach for Domain Adaptationslides ] [ Proof by Twitter ]
Data Intelligence Group Seminar (Laboratoire Hubert-Curien / Université Jean-Monnet, St-Étienne, France)

01/03/2016 : A Representation Learning Approach for Domain Adaptationslides ]
TAO Seminars (INRIA Saclay / CNRS / Université Paris-Sud, Orsay, France)

25/11/2015 : PAC-Bayesian Theory and Domain Adaptation Algorithmsslides ]
SIERRA Seminars (INRIA Paris / CNRS / ENS, Paris, France)

13/12/2014 : Domain-Adversarial Neural Networksslides ] [ workshop paper ]
NIPS 2014 Workshop on Transfer and Multi-task learning: Theory Meets Practice (Montreal, Quebec, Canada)

07/12/2012 : PAC-Bayesian Learning and Domain Adaptationslides ]
NIPS 2012 Workshop: Multi-trade-off in Machine Learning (Lake Tahoe, Nevada, US)

05/04/2013 : L'adaptation de domaine en apprentissage automatique: introduction et approche PAC-Bayésienneslides (french) ]
Séminaires du département d'informatique et de génie logiciel (Université Laval, Quebec, Canada)

09/10/2012 : A Pseudo-Boolean Set Covering Machineslides ]
18th International Conference on Principles and Practice of Constraint Programming (Quebec city, Quebec, Canada)

03/04/2009 : Rudiments de l'apprentissage automatique et de la classification (ainsi que quelques notions plus avancées!)slides (french) ]
Séminaires de l'Association des étudiant(e)s gradué(e)s en informatique à Laval (Université Laval, Québec, Canada)


Code

Machine Learning Algorithms
PAC-Bayesian Bounds Computation

Teaching / Enseignement (in French)