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

Pascal Germain , Amaury Habrard , François Laviolette , Emilie Morvant
NIPS 2014 Workshop on Transfer and Multi-task learning: Theory Meets Practice, Dec 2014, Montréal, Canada
Conference papers hal-01093565v2
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Une analyse PAC-Bayésienne de l'adaptation de domaine et sa spécialisation aux classifieurs linéaires

Pascal Germain , Amaury Habrard , François Laviolette , Emilie Morvant
Conférence sur l'apprentissage automatique, Jul 2013, Villeneuve d'Ascq, France. pp.3
Conference papers hal-00850242v1

Interpreting Neural Networks as Majority Votes through the PAC-Bayesian Theory

Paul Viallard , Rémi Emonet , Pascal Germain , Amaury Habrard , Emilie Morvant
Workshop on Machine Learning with guarantees @ NeurIPS 2019, Dec 2019, Vancouver, Canada
Conference papers hal-02335762v1
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PAC-Bayesian Bounds based on the Rényi Divergence

Luc Bégin , Pascal Germain , François Laviolette , Jean-Francis Roy
International Conference on Artificial Intelligence and Statistics (AISTATS 2016), May 2016, Cadiz, Spain
Conference papers hal-01384783v1
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A New PAC-Bayesian Perspective on Domain Adaptation

Pascal Germain , Amaury Habrard , François Laviolette , Emilie Morvant
33rd International Conference on Machine Learning (ICML 2016), Jun 2016, New York, NY, United States
Conference papers hal-01307045v1

Domain-Adversarial Training of Neural Networks

Yaroslav Ganin , Evgeniya Ustinova , Hana Ajakan , Pascal Germain , Hugo Larochelle , et al.
Gabriela Csurka. Domain Adaptation in Computer Vision Applications, Springer, 2017, Advances in Computer Vision and Pattern Recognition, 978-3-319-58346-4. ⟨10.1007/978-3-319-58347-1⟩
Book sections hal-01624607v1

Revisite des "random Fourier features" basée sur l'apprentissage PAC-Bayésien via des points d'intérêts

Léo Gautheron , Pascal Germain , Amaury Habrard , Gaël Letarte , Emilie Morvant , et al.
CAp 2019 - Conférence sur l'Apprentissage automatique, Jul 2019, Toulouse, France
Conference papers hal-02148600v1
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Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks

Gaël Letarte , Pascal Germain , Benjamin Guedj , François Laviolette
NeurIPS 2019 - Thirty-third Conference on Neural Information Processing Systems, Dec 2019, Vancouver, Canada
Conference papers hal-02139432v2

Dérandomisation des Bornes PAC-Bayésiennes

Paul Viallard , Pascal Germain , Emilie Morvant
CAp 2021, Jun 2021, St Etienne, France
Conference papers hal-03328677v1
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A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers

Pascal Germain , Amaury Habrard , François Laviolette , Emilie Morvant
International Conference on Machine Learning 2013, Jun 2013, Atlanta, United States. pp.738-746
Conference papers hal-00822685v1
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A New PAC-Bayesian View of Domain Adaptation

Pascal Germain , François Laviolette , Amaury Habrard , Emilie Morvant
NIPS 2015 Workshop on Transfer and Multi-Task Learning: Trends and New Perspectives, Dec 2015, Montréal, Canada
Conference papers hal-01223164v1

Apprentissage de Vote de Majorité par Minimisation d'une C-Borne PAC-Bayésienne

Paul Viallard , Pascal Germain , Emilie Morvant
CAp 2021, Jun 2021, St Etienne, France
Conference papers hal-03328689v1
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Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior

Gaël Letarte , Emilie Morvant , Pascal Germain
The 22nd International Conference on Artificial Intelligence and Statistics, Apr 2019, Naha, Japan
Conference papers hal-01908555v2
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PAC-Bayesian Theory Meets Bayesian Inference

Pascal Germain , Francis Bach , Alexandre Lacoste , Simon Lacoste-Julien
Neural Information Processing Systems (NIPS 2016), Dec 2016, Barcelone, Spain. pp.1876-1884
Conference papers hal-01324072v3

Une borne PAC-Bayésienne en espérance et son extension à l'apprentissage multivues

Anil Goyal , Emilie Morvant , Pascal Germain
Conférence Francophone sur l'Apprentissage Automatique (CAp), Jun 2017, Grenoble, France
Conference papers hal-01529219v1

PAC-Bayesian Analysis for a two-step Hierarchical Mutliview Learning Approach

Anil Goyal , Emilie Morvant , Pascal Germain , Massih-Reza Amini
27th European Conference on Machine Learning , Jul 2017, Skpoje, Macedonia
Conference papers hal-01769773v1
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Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting

Léo Gautheron , Pascal Germain , Amaury Habrard , Emilie Morvant , Marc Sebban , et al.
2019
Preprints, Working Papers, ... hal-02148618v1
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Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-trained Source Models

Luxin Zhang , Pascal Germain , Yacine Kessaci , Christophe Biernacki
36th AAAI Conférence on Artificial Intelligence, Feb 2022, Vancouver, Canada
Conference papers hal-03505639v1
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PAC-Bayesian Contrastive Unsupervised Representation Learning

Kento Nozawa , Pascal Germain , Benjamin Guedj
UAI 2020 - Conference on Uncertainty in Artificial Intelligence, Aug 2020, Toronto, Canada
Conference papers hal-02401282v1
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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 2021, Sep 2021, Bilbao, Spain
Conference papers hal-03208948v2
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PAC-Bayesian Learning and Domain Adaptation

Pascal Germain , Amaury Habrard , François Laviolette , Emilie Morvant
Multi-Trade-offs in Machine Learning, NIPS 2012 Workshop, Dec 2012, Lake Tahoe, United States
Conference papers hal-00749366v1
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PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers

Pascal Germain , Amaury Habrard , François Laviolette , Emilie Morvant
[Research Report] Université Jean Monnet, Saint-Étienne (42); Département d'Informatique et de Génie Logiciel, Université Laval (Québec); ENS Paris; IST Austria. 2016
Reports hal-01134246v3

Théorèmes PAC-Bayésiens pour l'apprentissage multi-vues

Anil Goyal , Emilie Morvant , Pascal Germain , Massih-Reza Amini
Conférence Francophone sur l'Apprentissage Automatique (CAp), Jul 2016, Marseille, France
Conference papers hal-01329763v1
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Landmark-based Ensemble Learning with Random Fourier Features and Gradient Boosting

Léo Gautheron , Pascal Germain , Amaury Habrard , Guillaume Metzler , Emilie Morvant , et al.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent, Belgium
Conference papers hal-02900044v1
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Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks

Gaël Letarte , Pascal Germain , Benjamin Guedj , François Laviolette
ML with guarantees -- NeurIPS 2019 workshop, Dec 2019, Vancouver, Canada
Poster communications hal-02482354v1
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PAC-Bayes and Domain Adaptation

Pascal Germain , Amaury Habrard , François Laviolette , Emilie Morvant
Neurocomputing, 2020, 379, pp.379-397. ⟨10.1016/j.neucom.2019.10.105⟩
Journal articles hal-01563152v3
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Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound

Valentina Zantedeschi , Paul Viallard , Emilie Morvant , Rémi Emonet , Amaury Habrard , et al.
NeurIPS, 2021, Online, France
Conference papers hal-03278470v1

PAC-Bayes Bounds for the Risk of the Majority Vote

Alexandre Lacasse , François Laviolette , Mario Marchand , Pascal Germain , Nicolas Usunier
Advances in Neural Information Processing Systems (NIPS'06), Dec 2006, Vancouver, Canada. pp.769-776
Conference papers hal-01352012v1
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A New PAC-Bayesian Perspective on Domain Adaptation

Pascal Germain , Amaury Habrard , François Laviolette , Emilie Morvant
[Research Report] Univ Lyon, UJM-Saint-Etienne, CNRS, Laboratoire Hubert Curien UMR 5516, F-42023 Saint-Etienne, France; Département d'informatique et de génie logiciel, Université Laval (Québec); INRIA - Sierra Project-Team, Ecole Normale Sup´erieure, Paris, France. 2015
Reports hal-01163722v3
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Improved PAC-Bayesian Bounds for Linear Regression

Vera Shalaeva , Alireza Fakhrizadeh Esfahani , Pascal Germain , Mihaly Petreczky
AAAI 2020 - Thirty-Fourth AAAI Conference on Artificial Intelligence, Feb 2020, New York, United States
Conference papers hal-02396556v1