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


Jun 23, 2021
Valentina Zantedeschi, Paul Viallard, Emilie Morvant, RĂ©mi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj


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


Apr 28, 2021
Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant


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A PAC-Bayes Analysis of Adversarial Robustness


Feb 19, 2021
Guillaume Vidot, Paul Viallard, Amaury Habrard, Emilie Morvant


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A General Framework for the Derandomization of PAC-Bayesian Bounds


Feb 17, 2021
Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant


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Multiview Variational Graph Autoencoders for Canonical Correlation Analysis


Oct 30, 2020
Yacouba Kaloga, Pierre Borgnat, Sundeep Prabhakar Chepuri, Patrice Abry, Amaury Habrard

* 4 pages, 3 figures, submitted 

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Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms


Oct 05, 2020
Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Amaury Habrard


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Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening


Jul 07, 2020
Louis Béthune, Yacouba Kaloga, Pierre Borgnat, Aurélien Garivier, Amaury Habrard

* 17 pages, 15 figures, submitted 

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A survey on domain adaptation theory


Apr 24, 2020
Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani


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Metric Learning from Imbalanced Data


Sep 04, 2019
LĂ©o Gautheron, Emilie Morvant, Amaury Habrard, Marc Sebban


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An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data


Sep 02, 2019
RĂ©mi Viola, RĂ©mi Emonet, Amaury Habrard, Guillaume Metzler, SĂ©bastien Riou, Marc Sebban

* In Proceedings of the 31 International Conference on Tools with Artificial Intelligence (ICTAI 2019) 

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


Jun 14, 2019
LĂ©o Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, Valentina Zantedeschi


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Near-lossless Binarization of Word Embeddings


May 28, 2018
Julien Tissier, Amaury Habrard, Christophe Gravier


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Joint Distribution Optimal Transportation for Domain Adaptation


Oct 22, 2017
Nicolas Courty, RĂ©mi Flamary, Amaury Habrard, Alain Rakotomamonjy

* Accepted for publication at NIPS 2017 

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Theoretical Analysis of Domain Adaptation with Optimal Transport


Jul 28, 2017
Ievgen Redko, Amaury Habrard, Marc Sebban


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PAC-Bayes and Domain Adaptation


Jul 17, 2017
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

* arXiv admin note: text overlap with arXiv:1503.06944 

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Similarity Learning for Time Series Classification


Oct 15, 2016
Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban

* Techreport 

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


Aug 09, 2016
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

* This report is a long version of our paper entitled A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers published in the proceedings of the International Conference on Machine Learning (ICML) 2013. We improved our main results, extended our experiments, and proposed an extension to multisource domain adaptation 

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A New PAC-Bayesian Perspective on Domain Adaptation


Jul 26, 2016
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

* Published at ICML 2016 

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Algorithmic Robustness for Learning via $(ε, γ, τ)$-Good Similarity Functions


Mar 31, 2015
Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, Massih-Reza Amini

* ICLR 2015 Workshop - accepted 

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


Jan 13, 2015
Pascal Germain, Amaury Habrard, Francois Laviolette, Emilie Morvant

* NIPS 2014 Workshop on Transfer and Multi-task learning: Theory Meets Practice, Dec 2014, Montr{\'e}al, Canada 

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Subspace Alignment For Domain Adaptation


Oct 23, 2014
Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars


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Robustness and Generalization for Metric Learning


Sep 29, 2014
Aurélien Bellet, Amaury Habrard

* Neurocomputing,151(1):259-267, 2015 
* 16 pages, to appear in Neurocomputing 

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Majority Vote of Diverse Classifiers for Late Fusion


Jun 19, 2014
Emilie Morvant, Amaury Habrard, Stéphane Ayache

* IAPR Joint International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recignition, Joensuu : Finland (2014) 

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A Survey on Metric Learning for Feature Vectors and Structured Data


Feb 12, 2014
Aurélien Bellet, Amaury Habrard, Marc Sebban

* Technical report, 59 pages. Changes in v2: fixed typos and improved presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new methods 

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Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning


Dec 21, 2013
François Denis, Mattias Gybels, Amaury Habrard

* Extended version of a paper to appear at ICML 2014 

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PAC-Bayesian Learning and Domain Adaptation


Dec 11, 2012
Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

* Multi-Trade-offs in Machine Learning, NIPS 2012 Workshop, Lake Tahoe : United States (2012) 
* https://sites.google.com/site/multitradeoffs2012/ 

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PAC-Bayesian Majority Vote for Late Classifier Fusion


Jul 04, 2012
Emilie Morvant, Amaury Habrard, Stéphane Ayache

* 7 pages, Research report 

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Similarity Learning for Provably Accurate Sparse Linear Classification


Jun 27, 2012
Aurelien Bellet, Amaury Habrard, Marc Sebban

* Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012) 

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Using Pseudo-Stochastic Rational Languages in Probabilistic Grammatical Inference


Nov 07, 2008
Amaury Habrard, Francois Denis, Yann Esposito

* 8th International Colloquium on Grammatical Inference (ICGI'06), Japan (2006) 

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On Probability Distributions for Trees: Representations, Inference and Learning


Jul 18, 2008
François Denis, Amaury Habrard, Rémi Gilleron, Marc Tommasi, Édouard Gilbert

* Dans NIPS Workshop on Representations and Inference on Probability Distributions (2007) 

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