Habrard, Amaury

49 publications

ICML 2025 A Bregman Proximal Viewpoint on Neural Operators Abdel-Rahim Mezidi, Jordan Patracone, Saverio Salzo, Amaury Habrard, Massimiliano Pontil, Rémi Emonet, Marc Sebban

ECML-PKDD 2025 Contextual Hypernetwork for Adaptive Prediction of Laser-Induced Colors on Quasi-Random Plasmonic Metasurfaces Thibault Girardin, Nathalie Destouches, Amaury Habrard

ECML-PKDD 2025 Provably Accurate Adaptive Sampling for Collocation Points in Physics-Informed Neural Networks Antoine Caradot, Rémi Emonet, Amaury Habrard, Abdel-Rahim Mezidi, Marc Sebban

MLJ 2024 A General Framework for the Practical Disintegration of PAC-Bayesian Bounds Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant

ECML-PKDD 2024 A Theoretically Grounded Extension of Universal Attacks from the Attacker's Viewpoint Jordan Patracone, Paul Viallard, Emilie Morvant, Gilles Gasso, Amaury Habrard, Stéphane Canu

ECML-PKDD 2024 Approximation Error of Sobolev Regular Functions with Tanh Neural Networks: Theoretical Impact on PINNs Benjamin Girault, Rémi Emonet, Amaury Habrard, Jordan Patracone, Marc Sebban

AISTATS 2024 Length Independent PAC-Bayes Bounds for Simple RNNs Volodimir Mitarchuk, Clara Lacroce, Rémi Eyraud, Rémi Emonet, Amaury Habrard, Guillaume Rabusseau

AISTATS 2024 Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi

TMLR 2024 On the Theoretical Limit of Gradient Descent for Simple Recurrent Neural Networks with Finite Precision Volodimir Mitarchuk, Rémi Emonet, Remi Eyraud, Amaury Habrard

ECML-PKDD 2023 Is My Neural Net Driven by the MDL Principle? Eduardo Brandao, Stefan Duffner, Rémi Emonet, Amaury Habrard, François Jacquenet, Marc Sebban

ICLR 2023 Proposal-Contrastive Pretraining for Object Detection from Fewer Data Quentin Bouniot, Romaric Audigier, Angelique Loesch, Amaury Habrard

WACV 2023 Towards Few-Annotation Learning for Object Detection: Are Transformer-Based Models More Efficient? Quentin Bouniot, Angélique Loesch, Romaric Audigier, Amaury Habrard

LoG 2022 A Simple Way to Learn Metrics Between Attributed Graphs Yacouba Kaloga, Pierre Borgnat, Amaury Habrard

ECCV 2022 Improving Few-Shot Learning Through Multi-Task Representation Learning Theory Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Amaury Habrard

NeurIPS 2021 A PAC-Bayes Analysis of Adversarial Robustness Paul Viallard, Eric Guillaume Vidot, Amaury Habrard, Emilie Morvant

NeurIPS 2021 Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj

ECML-PKDD 2021 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 2020 Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban, Valentina Zantedeschi

IJCAI 2020 Learning from Few Positives: A Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Marc Sebban

IJCAI 2019 Differentially Private Optimal Transport: Application to Domain Adaptation Nam Lê Tien, Amaury Habrard, Marc Sebban

AISTATS 2019 From Cost-Sensitive Classification to Tight F-Measure Bounds Kevin Bascol, Rémi Emonet, Elisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban

AAAI 2019 Near-Lossless Binarization of Word Embeddings Julien Tissier, Christophe Gravier, Amaury Habrard

MLJ 2019 On the Analysis of Adaptability in Multi-Source Domain Adaptation Ievgen Redko, Amaury Habrard, Marc Sebban

ECML-PKDD 2017 Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection Jordan Fréry, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton

NeurIPS 2017 Joint Distribution Optimal Transportation for Domain Adaptation Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy

ECML-PKDD 2017 Theoretical Analysis of Domain Adaptation with Optimal Transport Ievgen Redko, Amaury Habrard, Marc Sebban

ICML 2016 A New PAC-Bayesian Perspective on Domain Adaptation Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

JMLR 2016 Dimension-Free Concentration Bounds on Hankel Matrices for Spectral Learning François Denis, Mattias Gybels, Amaury Habrard

NeurIPS 2016 Mapping Estimation for Discrete Optimal Transport Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard

ICML 2015 A Theoretical Analysis of Metric Hypothesis Transfer Learning Michaël Perrot, Amaury Habrard

ICLR 2015 Algorithmic Robustness for Semi-Supervised (ε, Γ, Τ)-Good Metric Learning Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, Massih-Reza Amini

ECML-PKDD 2015 Joint Semi-Supervised Similarity Learning for Linear Classification Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban

NeurIPS 2015 Regressive Virtual Metric Learning Michaël Perrot, Amaury Habrard

ICML 2014 Dimension-Free Concentration Bounds on Hankel Matrices for Spectral Learning François Denis, Mattias Gybels, Amaury Habrard

MLJ 2014 Learning a Priori Constrained Weighted Majority Votes Aurélien Bellet, Amaury Habrard, Emilie Morvant, Marc Sebban

ECCV 2014 Modeling Perceptual Color Differences by Local Metric Learning Michaël Perrot, Amaury Habrard, Damien Muselet, Marc Sebban

ICML 2013 A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant

ECML-PKDD 2013 Boosting for Unsupervised Domain Adaptation Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban

ICCV 2013 Unsupervised Visual Domain Adaptation Using Subspace Alignment Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars

MLJ 2012 Good Edit Similarity Learning by Loss Minimization Aurélien Bellet, Amaury Habrard, Marc Sebban

ICML 2012 Similarity Learning for Provably Accurate Sparse Linear Classification Aurélien Bellet, Amaury Habrard, Marc Sebban

ECML-PKDD 2011 Learning Good Edit Similarities with Generalization Guarantees Aurélien Bellet, Amaury Habrard, Marc Sebban

ALT 2010 A Spectral Approach for Probabilistic Grammatical Inference on Trees Raphaël Bailly, Amaury Habrard, François Denis

JMLR 2010 Using Contextual Representations to Efficiently Learn Context-Free Languages Alexander Clark, Rémi Eyraud, Amaury Habrard

ECML-PKDD 2008 SEDiL: Software for Edit Distance Learning Laurent Boyer, Yann Esposito, Amaury Habrard, José Oncina, Marc Sebban

ALT 2007 Learning Rational Stochastic Tree Languages François Denis, Amaury Habrard

COLT 2006 Learning Rational Stochastic Languages François Denis, Yann Esposito, Amaury Habrard

ECML-PKDD 2006 Learning Stochastic Tree Edit Distance Marc Bernard, Amaury Habrard, Marc Sebban

ECML-PKDD 2003 Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference Amaury Habrard, Marc Bernard, Marc Sebban