Pascal Germain

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Membre Académique Associé
Pascal Germain
Professeur adjoint, Université Laval
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

2021-06

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
arXiv preprint arXiv:2106.12535
(2021-06-23)
dblp.uni-trier.dePDF

2021-04

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
arXiv preprint arXiv:2104.13626
(2021-04-28)
dblp.uni-trier.dePDF

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.eduPDF

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

2020-04

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

2019-10

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

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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