Pascal Germain

发表

From PAC-Bayes Bounds to KL Regularization

François Laviolette, Mario Marchand, Pascal Germain, 2009, NIPS.

A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers

François Laviolette, Amaury Habrard, Emilie Morvant, 2013, ICML.

Improved PAC-Bayesian Bounds for Linear Regression pdf

Pascal Germain, Alireza Fakhrizadeh Esfahani, Vera Shalaeva, 2019, AAAI.

PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers pdf

François Laviolette, Amaury Habrard, Emilie Morvant, 2015, ArXiv.

Implicit Variational Inference: the Parameter and the Predictor Space pdf

Patrick Dallaire, Pascal Germain, Yann Pequignot, 2020, ArXiv.

A Pseudo-Boolean Set Covering Machine

François Laviolette, Claude-Guy Quimper, Pascal Germain, 2012, CP.

An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context pdf

François Laviolette, Amaury Habrard, Emilie Morvant, 2014, NIPS 2014.

Landmark-based Ensemble Learning with Random Fourier Features and Gradient Boosting

Marc Sebban, Amaury Habrard, Emilie Morvant, 2020 .

PAC-Bayesian Theory Meets Bayesian Inference

Alexandre Lacoste, Francis R. Bach, Pascal Germain, 2016, NIPS.

A New PAC-Bayesian View of Domain Adaptation

François Laviolette, Amaury Habrard, Emilie Morvant, 2015, NIPS 2015.

Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting pdf

Marc Sebban, Amaury Habrard, Emilie Morvant, 2019, ArXiv.

PAC-Bayesian Bounds based on the Rényi Divergence

François Laviolette, Pascal Germain, Jean-Francis Roy, 2016, AISTATS.

Domain Adaptation from a Pre-trained Source Model: Application on fraud detection tasks

Luxin Zhang, Christophe Biernacki, Yacine Kessaci, 2019 .

PAC-Bayesian Analysis for a Two-Step Hierarchical Multiview Learning Approach

Massih-Reza Amini, Emilie Morvant, Anil Goyal, 2016, ECML/PKDD.

Supplementary Material to A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classiers

Amaury Habrard, Emilie Morvant, Pascal Germain, 2004 .

PAC-Bayesian learning of linear classifiers

François Laviolette, Mario Marchand, Pascal Germain, 2009, ICML '09.

Domain-Adversarial Neural Networks pdf

François Laviolette, Hugo Larochelle, Mario Marchand, 2014, ArXiv.

Domain-Adversarial Training of Neural Networks pdf

François Laviolette, Hugo Larochelle, Mario Marchand, 2015, J. Mach. Learn. Res..

Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks

Pascal Germain, Franccois Laviolette, Benjamin Guedj, 2019, NeurIPS.

Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior pdf

Emilie Morvant, Pascal Germain, Gaël Letarte, 2019, AISTATS.

PAC-Bayesian Learning and Domain Adaptation pdf

François Laviolette, Amaury Habrard, Emilie Morvant, 2012, NIPS 2012.

Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm

François Laviolette, Mario Marchand, Pascal Germain, 2015, J. Mach. Learn. Res..

Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters

Massih-Reza Amini, Emilie Morvant, Anil Goyal, 2019, Neurocomputing.

A PAC-Bayes Sample-compression Approach to Kernel Methods

François Laviolette, Alexandre Lacoste, Mario Marchand, 2011, ICML.

PAC-Bayesian Contrastive Unsupervised Representation Learning pdf

Pascal Germain, Benjamin Guedj, Kento Nozawa, 2019, UAI.

PAC-Bayesian Theory for Transductive Learning

François Laviolette, Pascal Germain, Jean-Francis Roy, 2014, AISTATS.

PAC-Bayes and domain adaptation

Amaury Habrard, Emilie Morvant, Pascal Germain, 2017, Neurocomputing.

A PAC-Bayes Risk Bound for General Loss Functions

François Laviolette, Mario Marchand, Pascal Germain, 2006, NIPS.

PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier

François Laviolette, Mario Marchand, Nicolas Usunier, 2006, NIPS.

A new PAC-Bayesian perspective on domain adaptation

François Laviolette, Amaury Habrard, Emilie Morvant, 2015, ICML 2016.

A General Framework for the Disintegration of PAC-Bayesian Bounds

Amaury Habrard, Emilie Morvant, Pascal Germain, 2021, 2102.08649.

Learning Aggregations of Binary Activated Neural Networks with Probabilities over Representations pdf

Pascal Germain, Franccois Laviolette, Benjamin Leblanc, 2021, ArXiv.

Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound

Amaury Habrard, Emilie Morvant, Pascal Germain, 2021, ECML/PKDD.

A General Framework for the Derandomization of PAC-Bayesian Bounds pdf

Amaury Habrard, Emilie Morvant, Pascal Germain, 2021, ArXiv.

Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound pdf

Amaury Habrard, Emilie Morvant, Pascal Germain, 2021, NeurIPS.