Pascal Germain

Mila > Équipe > Pascal Germain
Membre Affilié Externe
Pascal Germain
Professeur adjoint, Université Laval, Chaire en IA Canada-CIFAR
Pascal Germain

Pascal Germain est professeur adjoint département d’informatique et de génie logiciel de l’Université Laval. Chercheur scientifique en apprentissage automatique, il exerçait ses fonctions jusqu’à tout récemment à l’Inria, l’institut national de recherche dédié aux sciences du numérique, en France. Ses domaines de recherche comprennent la théorie statistique de l’apprentissage, dont la théorie PAC-bayésienne, et les algorithmes d’apprentissage.

Publications

2021-12

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

2021-10

Learning Aggregations of Binary Activated Neural Networks with Probabilities over Representations
Louis Fortier-Dubois, Gaël Letarte, Benjamin Leblanc, François Laviolette and Pascal Germain
arXiv preprint arXiv:2110.15137
(2021-10-28)
dblp.uni-trier.dePDF
A General Framework for the Disintegration of PAC-Bayesian Bounds
Paul Viallard, Pascal Germain, Amaury Habrard and Emilie Morvant
arXiv: Machine Learning
(2021-10-08)
hal.archives-ouvertes.frPDF

2021-09

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

2021-06

Apprentissage de Vote de Majorité par Minimisation d'une C-Borne PAC-Bayésienne
Paul Viallard, Pascal Germain and Emilie Morvant
CAp 2021
(2021-06-14)
hal.archives-ouvertes.fr
Dérandomisation des Bornes PAC-Bayésiennes
Paul Viallard, Pascal Germain and Emilie Morvant
CAp 2021
(2021-06-14)
hal.archives-ouvertes.fr

2021-02

A General Framework for the Derandomization of PAC-Bayesian Bounds
Paul Viallard, Pascal Germain, Amaury Habrard and Emilie Morvant
arXiv preprint arXiv:2102.08649
(2021-02-16)
ui.adsabs.harvard.edu

2020-10

Implicit Variational Inference: the Parameter and the Predictor Space.
Yann Pequignot, Mathieu Alain, Patrick Dallaire, Alireza Yeganehparast, Pascal Germain, Josée Desharnais and François Laviolette
arXiv preprint arXiv:2010.12995
(2020-10-24)
ui.adsabs.harvard.eduPDF

2020-09

Target to Source Coordinate-wise Adaptation of Pre-trained Models
Luxin Zhang, Pascal Germain, Yacine Kessaci and Christophe Biernacki
ECML 2020
(2020-09-14)
hal.archives-ouvertes.frPDF
Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting
Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban and Valentina Zantedeschi
ECML 2020
(2020-09-14)
hal.archives-ouvertes.frPDF

2020-08

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

2020-04

Improved PAC-Bayesian Bounds for Linear Regression
Vera Shalaeva, Alireza Fakhrizadeh Esfahani, Pascal Germain and Mihaly Petreczky

2020-02

PAC-Bayes and Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette and Emilie Morvant
Neurocomputing
(2020-02-28)
www.sciencedirect.comPDF

2019-12

Domain Adaptation from a Pre-trained Source Model: Application on fraud detection tasks
Luxin Zhang, Christophe Biernacki, Pascal Germain and Yacine Kessaci
CMStatistics 2019 - 12th International Conference of the ERCIM WG on Computational and Methodological Statistics
(2019-12-14)
hal.archives-ouvertes.fr
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
Gaël Letarte, Pascal Germain, Benjamin Guedj and Francois Laviolette
NEURIPS 2019
(2019-12-08)
papers.nips.ccPDF

2019-09

Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
Anil Goyal, Emilie Morvant, Pascal Germain and Massih-Reza Amini
Neurocomputing
(2019-09-17)
www.sciencedirect.comPDF

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