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2020 – today
- 2024
[j29]Damien Robissout, Lilian Bossuet, Amaury Habrard:
Scoring the predictions: a way to improve profiling side-channel attacks. J. Cryptogr. Eng. 14(3): 513-535 (2024)- [j28]Paul Viallard
, Pascal Germain, Amaury Habrard, Emilie Morvant:
A general framework for the practical disintegration of PAC-Bayesian bounds. Mach. Learn. 113(2): 519-604 (2024) - [c70]Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi:
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures. AISTATS 2024: 3007-3015 - [c69]Volodimir Mitarchuk, Clara Lacroce, Rémi Eyraud, Rémi Emonet, Amaury Habrard, Guillaume Rabusseau:
Length independent PAC-Bayes bounds for Simple RNNs. AISTATS 2024: 3547-3555 - [c68]Jorge Azorín López, Marc Sebban, Nahuel E. Garcia-D'Urso, Amaury Habrard, Andrés Fuster Guilló:
Generative shape deformation with optimal transport using learned transformations. IJCNN 2024: 1-8 - [c67]Benjamin Girault, Rémi Emonet, Amaury Habrard, Jordan Patracone, Marc Sebban:
Approximation Error of Sobolev Regular Functions with Tanh Neural Networks: Theoretical Impact on PINNs. ECML/PKDD (4) 2024: 266-282 - [c66]Jordan Patracone, Paul Viallard, Emilie Morvant, Gilles Gasso, Amaury Habrard, Stéphane Canu:
A Theoretically Grounded Extension of Universal Attacks from the Attacker's Viewpoint. ECML/PKDD (4) 2024: 283-300 - [i40]Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi:
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures. CoRR abs/2402.13285 (2024) - [i39]Damien Robissout, Lilian Bossuet, Amaury Habrard:
Scoring the predictions: a way to improve profiling side-channel attacks. IACR Cryptol. ePrint Arch. 2024: 558 (2024) - 2023
- [j27]Gabriel Zaid, Lilian Bossuet, Mathieu Carbone, Amaury Habrard, Alexandre Venelli:
Conditional Variational AutoEncoder based on Stochastic Attacks. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2023(2): 310-357 (2023) - [c65]Quentin Bouniot, Romaric Audigier, Angélique Loesch, Amaury Habrard:
Proposal-Contrastive Pretraining for Object Detection from Fewer Data. ICLR 2023 - [c64]Eduardo Brandao, Stefan Duffner, Rémi Emonet, Amaury Habrard, François Jacquenet, Marc Sebban:
Is My Neural Net Driven by the MDL Principle? ECML/PKDD (2) 2023: 173-189 - [c63]Nahuel E. Garcia-D'Urso, Pablo Ramon Guevara, Jorge Azorín López, Marc Sebban, Amaury Habrard, Andrés Fuster Guilló:
Predictive Modeling of Body Shape Changes in Individuals on Dietetic Treatment Using Recurrent Networks. UCAmI (2) 2023: 100-111 - [c62]Quentin Bouniot, Angélique Loesch, Amaury Habrard, Romaric Audigier:
Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient? WACV 2023: 75-84 - [i38]Quentin Bouniot, Romaric Audigier, Angélique Loesch, Amaury Habrard:
Proposal-Contrastive Pretraining for Object Detection from Fewer Data. CoRR abs/2310.16835 (2023) - [i37]Quentin Bouniot, Angélique Loesch, Romaric Audigier, Amaury Habrard:
Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ? CoRR abs/2310.19936 (2023) - 2022
- [j26]Eduardo Brandao, Jean-Philippe Colombier, Stefan Duffner, Rémi Emonet, Florence Garrelie, Amaury Habrard, François Jacquenet, Anthony Nakhoul, Marc Sebban:
Learning PDE to Model Self-Organization of Matter. Entropy 24(8): 1096 (2022) - [j25]Rémi Viola, Léo Gautheron, Amaury Habrard, Marc Sebban:
MetaAP: A meta-tree-based ranking algorithm optimizing the average precision from imbalanced data. Pattern Recognit. Lett. 161: 161-167 (2022) - [c61]Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Amaury Habrard:
Improving Few-Shot Learning Through Multi-task Representation Learning Theory. ECCV (20) 2022: 435-452 - [c60]Yacouba Kaloga, Pierre Borgnat, Amaury Habrard:
A Simple Way to Learn Metrics Between Attributed Graphs. LoG 2022: 25 - [i36]Yacouba Kaloga, Pierre Borgnat, Amaury Habrard:
A simple way to learn metrics between attributed graphs. CoRR abs/2209.12727 (2022) - [i35]Gabriel Zaid, Lilian Bossuet, Mathieu Carbone, Amaury Habrard, Alexandre Venelli:
Conditional Variational AutoEncoder based on Stochastic Attack. IACR Cryptol. ePrint Arch. 2022: 232 (2022) - 2021
- [j24]Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Sébastien Riou, Marc Sebban:
A Nearest Neighbor Algorithm for Imbalanced Classification. Int. J. Artif. Intell. Tools 30(3): 2150013:1-2150013:27 (2021) - [j23]Damien Robissout, Lilian Bossuet, Amaury Habrard, Vincent Grosso:
Improving Deep Learning Networks for Profiled Side-channel Analysis Using Performance Improvement Techniques. ACM J. Emerg. Technol. Comput. Syst. 17(3): 41:1-41:30 (2021) - [j22]Jorge Azorín López, Marc Sebban, Andrés Fuster Guilló, Marcelo Saval-Calvo, Amaury Habrard:
Iterative multilinear optimization for planar model fitting under geometric constraints. PeerJ Comput. Sci. 7: e691 (2021) - [j21]Yacouba Kaloga, Pierre Borgnat, Sundeep Prabhakar Chepuri, Patrice Abry, Amaury Habrard:
Variational graph autoencoders for multiview canonical correlation analysis. Signal Process. 188: 108182 (2021) - [j20]Gabriel Zaid, Lilian Bossuet, François Dassance, Amaury Habrard, Alexandre Venelli:
Ranking Loss: Maximizing the Success Rate in Deep Learning Side-Channel Analysis. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2021(1): 25-55 (2021) - [j19]Gabriel Zaid, Lilian Bossuet, Amaury Habrard, Alexandre Venelli:
Efficiency through Diversity in Ensemble Models applied to Side-Channel Attacks - A Case Study on Public-Key Algorithms -. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2021(3): 60-96 (2021) - [c59]Yacouba Kaloga, Pierre Borgnat, Sundeep Prabhakar Chepuri, Patrice Abry, Amaury Habrard:
Multiview Variational Graph Autoencoders for Canonical Correlation Analysis. ICASSP 2021: 5320-5324 - [c58]Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj:
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. NeurIPS 2021: 455-467 - [c57]Paul Viallard, Guillaume Vidot, Amaury Habrard, Emilie Morvant:
A PAC-Bayes Analysis of Adversarial Robustness. NeurIPS 2021: 14421-14433 - [c56]Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant:
Self-bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound. ECML/PKDD (2) 2021: 167-183 - [i34]Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant:
A General Framework for the Derandomization of PAC-Bayesian Bounds. CoRR abs/2102.08649 (2021) - [i33]Guillaume Vidot, Paul Viallard, Amaury Habrard, Emilie Morvant:
A PAC-Bayes Analysis of Adversarial Robustness. CoRR abs/2102.11069 (2021) - [i32]Paul Viallard, Pascal Germain, Amaury Habrard, Emilie Morvant:
Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-Bound. CoRR abs/2104.13626 (2021) - [i31]Valentina Zantedeschi, Paul Viallard, Emilie Morvant, Rémi Emonet, Amaury Habrard, Pascal Germain, Benjamin Guedj:
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound. CoRR abs/2106.12535 (2021) - [i30]Gabriel Zaid, Lilian Bossuet, Amaury Habrard, Alexandre Venelli:
Efficiency through Diversity in Ensemble Models applied to Side-Channel Attacks - A Case Study on Public-Key Algorithms -. IACR Cryptol. ePrint Arch. 2021: 909 (2021) - [i29]Damien Robissout, Lilian Bossuet, Amaury Habrard, Vincent Grosso:
Improving Deep Learning Networks for Profiled Side-Channel Analysis Using Performance Improvement Techniques. IACR Cryptol. ePrint Arch. 2021: 1546 (2021) - 2020
- [j18]Louis Béthune, Yacouba Kaloga, Pierre Borgnat, Aurélien Garivier, Amaury Habrard:
Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening. Algorithms 13(9): 206 (2020) - [j17]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
PAC-Bayes and domain adaptation. Neurocomputing 379: 379-397 (2020) - [j16]Léo Gautheron, Amaury Habrard, Emilie Morvant, Marc Sebban:
Metric Learning from Imbalanced Data with Generalization Guarantees. Pattern Recognit. Lett. 133: 298-304 (2020) - [j15]Gabriel Zaid, Lilian Bossuet, Amaury Habrard, Alexandre Venelli:
Methodology for Efficient CNN Architectures in Profiling Attacks. IACR Trans. Cryptogr. Hardw. Embed. Syst. 2020(1): 1-36 (2020) - [c55]Damien Robissout, Gabriel Zaid, Brice Colombier, Lilian Bossuet, Amaury Habrard:
Online Performance Evaluation of Deep Learning Networks for Profiled Side-Channel Analysis. COSADE 2020: 200-218 - [c54]Ayman Alazizi, Amaury Habrard, François Jacquenet, Liyun He-Guelton, Frédéric Oblé:
Dual Sequential Variational Autoencoders for Fraud Detection. IDA 2020: 14-26 - [c53]Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Marc Sebban:
Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data. IJCAI 2020: 2155-2161 - [c52]Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, Marc Sebban, Valentina Zantedeschi:
Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting. ECML/PKDD (3) 2020: 141-157 - [i28]Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani:
A survey on domain adaptation theory. CoRR abs/2004.11829 (2020) - [i27]Louis Béthune, Yacouba Kaloga, Pierre Borgnat, Aurélien Garivier, Amaury Habrard:
Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening. CoRR abs/2007.03373 (2020) - [i26]Quentin Bouniot, Ievgen Redko, Romaric Audigier, Angélique Loesch, Amaury Habrard:
Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms. CoRR abs/2010.01992 (2020) - [i25]Yacouba Kaloga, Pierre Borgnat, Sundeep Prabhakar Chepuri, Patrice Abry, Amaury Habrard:
Multiview Variational Graph Autoencoders for Canonical Correlation Analysis. CoRR abs/2010.16132 (2020) - [i24]Damien Robissout, Gabriel Zaid, Brice Colombier, Lilian Bossuet, Amaury Habrard:
Online Performance Evaluation of Deep Learning Networks for Side-Channel Analysis. IACR Cryptol. ePrint Arch. 2020: 39 (2020) - [i23]Gabriel Zaid, Lilian Bossuet, Amaury Habrard, Alexandre Venelli:
Understanding Methodology for Efficient CNN Architectures in Profiling Attacks. IACR Cryptol. ePrint Arch. 2020: 757 (2020) - [i22]Gabriel Zaid, Lilian Bossuet, François Dassance, Amaury Habrard, Alexandre Venelli:
Ranking Loss: Maximizing the Success Rate in Deep Learning Side-Channel Analysis. IACR Cryptol. ePrint Arch. 2020: 872 (2020)
2010 – 2019
- 2019
- [j14]Ievgen Redko, Amaury Habrard, Marc Sebban:
On the analysis of adaptability in multi-source domain adaptation. Mach. Learn. 108(8-9): 1635-1652 (2019) - [j13]Tien-Nam Le, Amaury Habrard, Marc Sebban:
Deep multi-Wasserstein unsupervised domain adaptation. Pattern Recognit. Lett. 125: 249-255 (2019) - [c51]Julien Tissier, Christophe Gravier, Amaury Habrard:
Near-Lossless Binarization of Word Embeddings. AAAI 2019: 7104-7111 - [c50]Kevin Bascol, Rémi Emonet, Élisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban:
From Cost-Sensitive to Tight F-measure Bounds. AISTATS 2019: 1245-1253 - [c49]Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Sébastien Riou, Marc Sebban:
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data. ICTAI 2019: 243-250 - [c48]Léo Gautheron, Amaury Habrard, Emilie Morvant, Marc Sebban:
Metric Learning from Imbalanced Data. ICTAI 2019: 923-930 - [c47]Ayman Alazizi, Amaury Habrard, François Jacquenet, Liyun He-Guelton, Frédéric Oblé, Wissam Siblini:
Anomaly Detection, Consider Your Dataset First An Illustration on Fraud Detection. ICTAI 2019: 1351-1355 - [c46]Nam Lê Tien, Amaury Habrard, Marc Sebban:
Differentially Private Optimal Transport: Application to Domain Adaptation. IJCAI 2019: 2852-2858 - [i21]Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, Valentina Zantedeschi:
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting. CoRR abs/1906.06203 (2019) - [i20]Rémi Viola, Rémi Emonet, Amaury Habrard, Guillaume Metzler, Sébastien Riou, Marc Sebban:
An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data. CoRR abs/1909.00693 (2019) - [i19]Léo Gautheron, Emilie Morvant, Amaury Habrard, Marc Sebban:
Metric Learning from Imbalanced Data. CoRR abs/1909.01651 (2019) - [i18]Gabriel Zaid, Lilian Bossuet, Amaury Habrard, Alexandre Venelli:
Methodology for Efficient CNN Architectures in Profiling Attacks. IACR Cryptol. ePrint Arch. 2019: 803 (2019) - 2018
- [j12]Guillaume Metzler, Xavier Badiche, Brahim Belkasmi, Élisa Fromont, Amaury Habrard, Marc Sebban:
Learning maximum excluding ellipsoids from imbalanced data with theoretical guarantees. Pattern Recognit. Lett. 112: 310-316 (2018) - [c45]Jordan Fréry, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton:
Online Non-linear Gradient Boosting in Multi-latent Spaces. IDA 2018: 99-110 - [c44]Guillaume Metzler, Xavier Badiche, Brahim Belkasmi, Élisa Fromont, Amaury Habrard, Marc Sebban:
Tree-Based Cost Sensitive Methods for Fraud Detection in Imbalanced Data. IDA 2018: 213-224 - [c43]Jordan Fréry, Amaury Habrard, Marc Sebban, Liyun He-Guelton:
Non-Linear Gradient Boosting for Class-Imbalance Learning. LIDTA@ECML/PKDD 2018: 38-51 - [i17]Julien Tissier, Amaury Habrard, Christophe Gravier:
Near-lossless Binarization of Word Embeddings. CoRR abs/1803.09065 (2018) - 2017
- [c42]Julien Tissier, Christophe Gravier, Amaury Habrard:
Dict2vec : Learning Word Embeddings using Lexical Dictionaries. EMNLP 2017: 254-263 - [c41]Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy:
Joint distribution optimal transportation for domain adaptation. NIPS 2017: 3730-3739 - [c40]Jordan Fréry, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton:
Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection. ECML/PKDD (1) 2017: 20-35 - [c39]Ievgen Redko, Amaury Habrard, Marc Sebban:
Theoretical Analysis of Domain Adaptation with Optimal Transport. ECML/PKDD (2) 2017: 737-753 - [p1]Basura Fernando, Rahaf Aljundi, Rémi Emonet, Amaury Habrard, Marc Sebban, Tinne Tuytelaars:
Unsupervised Domain Adaptation Based on Subspace Alignment. Domain Adaptation in Computer Vision Applications 2017: 81-94 - [i16]Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy:
Joint Distribution Optimal Transportation for Domain Adaptation. CoRR abs/1705.08848 (2017) - 2016
- [j11]Aurélien Bellet, José Francisco Bernabeu, Amaury Habrard, Marc Sebban:
Learning discriminative tree edit similarities for linear classification - Application to melody recognition. Neurocomputing 214: 155-161 (2016) - [j10]François Denis, Mattias Gybels, Amaury Habrard:
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning. J. Mach. Learn. Res. 17: 31:1-31:32 (2016) - [j9]Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban:
A new boosting algorithm for provably accurate unsupervised domain adaptation. Knowl. Inf. Syst. 47(1): 45-73 (2016) - [c38]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
A New PAC-Bayesian Perspective on Domain Adaptation. ICML 2016: 859-868 - [c37]Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard:
Mapping Estimation for Discrete Optimal Transport. NIPS 2016: 4197-4205 - [i15]Ievgen Redko, Amaury Habrard, Marc Sebban:
Theoretical Analysis of Domain Adaptation with Optimal Transport. CoRR abs/1610.04420 (2016) - [i14]Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban:
Similarity Learning for Time Series Classification. CoRR abs/1610.04783 (2016) - 2015
- [b1]Aurélien Bellet, Amaury Habrard, Marc Sebban:
Metric Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2015, ISBN 978-3-031-00444-5 - [j8]Aurélien Bellet, Amaury Habrard:
Robustness and generalization for metric learning. Neurocomputing 151: 259-267 (2015) - [c36]Michaël Perrot, Amaury Habrard:
A Theoretical Analysis of Metric Hypothesis Transfer Learning. ICML 2015: 1708-1717 - [c35]Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, Massih-Reza Amini:
Algorithmic Robustness for Semi-Supervised (ε, γ, τ) -Good Metric Learning. ICONIP (1) 2015: 253-263 - [c34]Michaël Perrot, Amaury Habrard:
Regressive Virtual Metric Learning. NIPS 2015: 1810-1818 - [c33]Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban:
Joint Semi-supervised Similarity Learning for Linear Classification. ECML/PKDD (1) 2015: 594-609 - [c32]Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, Massih-Reza Amini:
Algorithmic Robustness for Semi-Supervised (ε, γ, τ)-Good Metric Learning. ICLR (Workshop) 2015 - [i13]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context. CoRR abs/1501.03002 (2015) - [i12]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers. CoRR abs/1503.06944 (2015) - [i11]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
A New PAC-Bayesian Perspective on Domain Adaptation. CoRR abs/1506.04573 (2015) - 2014
- [j7]Aurélien Bellet, Amaury Habrard, Emilie Morvant, Marc Sebban:
Learning a priori constrained weighted majority votes. Mach. Learn. 97(1-2): 129-154 (2014) - [c31]Michaël Perrot, Amaury Habrard, Damien Muselet, Marc Sebban:
Modeling Perceptual Color Differences by Local Metric Learning. ECCV (5) 2014: 96-111 - [c30]Mattias Gybels, François Denis, Amaury Habrard:
Some improvements of the spectral learning approach for probabilistic grammatical inference. ICGI 2014: 64-78 - [c29]François Denis, Mattias Gybels, Amaury Habrard:
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning. ICML 2014: 449-457 - [c28]Emilie Morvant, Amaury Habrard, Stéphane Ayache:
Majority Vote of Diverse Classifiers for Late Fusion. S+SSPR 2014: 153-162 - [i10]Emilie Morvant, Amaury Habrard, Stéphane Ayache:
Majority Vote of Diverse Classifiers for Late Fusion. CoRR abs/1404.7796 (2014) - [i9]Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars:
Subspace Alignment For Domain Adaptation. CoRR abs/1409.5241 (2014) - 2013
- [j6]Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban:
Iterative Self-Labeling Domain Adaptation for Linear Structured Image Classification. Int. J. Artif. Intell. Tools 22(5) (2013) - [c27]Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars:
Unsupervised Visual Domain Adaptation Using Subspace Alignment. ICCV 2013: 2960-2967 - [c26]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers. ICML (3) 2013: 738-746 - [c25]Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban:
Boosting for Unsupervised Domain Adaptation. ECML/PKDD (2) 2013: 433-448 - [i8]Aurélien Bellet, Amaury Habrard, Marc Sebban:
A Survey on Metric Learning for Feature Vectors and Structured Data. CoRR abs/1306.6709 (2013) - [i7]François Denis, Mattias Gybels, Amaury Habrard:
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning. CoRR abs/1312.6282 (2013) - 2012
- [j5]Emilie Morvant, Amaury Habrard, Stéphane Ayache:
Parsimonious unsupervised and semi-supervised domain adaptation with good similarity functions. Knowl. Inf. Syst. 33(2): 309-349 (2012) - [j4]Aurélien Bellet, Amaury Habrard, Marc Sebban:
Good edit similarity learning by loss minimization. Mach. Learn. 89(1-2): 5-35 (2012) - [c24]Aurélien Bellet, Amaury Habrard, Marc Sebban:
Similarity Learning for Provably Accurate Sparse Linear Classification. ICML 2012 - [c23]Leonor Becerra-Bonache, Élisa Fromont, Amaury Habrard, Michaël Perrot, Marc Sebban:
Speeding Up Syntactic Learning Using Contextual Information. ICGI 2012: 49-53 - [i6]Emilie Morvant, Amaury Habrard, Stéphane Ayache:
PAC-Bayesian Majority Vote for Late Classifier Fusion. CoRR abs/1207.1019 (2012) - [i5]Aurélien Bellet, Amaury Habrard:
Robustness and Generalization for Metric Learning. CoRR abs/1209.1086 (2012) - [i4]Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant:
PAC-Bayesian Learning and Domain Adaptation. CoRR abs/1212.2340 (2012) - 2011
- [c22]Emilie Morvant, Amaury Habrard, Stéphane Ayache:
Sparse Domain Adaptation in Projection Spaces Based on Good Similarity Functions. ICDM 2011: 457-466 - [c21]Aurélien Bellet, Marc Sebban, Amaury Habrard:
An Experimental Study on Learning with Good Edit Similarity Functions. ICTAI 2011: 126-133 - [c20]Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban:
Domain Adaptation with Good Edit Similarities: A Sparse Way to Deal with Scaling and Rotation Problems in Image Classification. ICTAI 2011: 181-188 - [c19]Aurélien Bellet, Amaury Habrard, Marc Sebban:
Learning Good Edit Similarities with Generalization Guarantees. ECML/PKDD (1) 2011: 188-203 - [c18]Emilie Morvant, Amaury Habrard, Stéphane Ayache:
On the Usefulness of Similarity Based Projection Spaces for Transfer Learning. SIMBAD 2011: 1-16 - [c17]Emilie Morvant, Stéphane Ayache, Amaury Habrard, Miriam Redi, Claudiu Tanase, Bernard Mérialdo, Bahjat Safadi, Franck Thollard, Nadia Derbas, Georges Quénot:
VideoSense at TRECVID 2011: Semantic Indexing from Light Similarity Functions-based Domain Adaptation with Stacking. TRECVID 2011 - 2010
- [j3]Alexander Clark, Rémi Eyraud, Amaury Habrard:
Using Contextual Representations to Efficiently Learn Context-Free Languages. J. Mach. Learn. Res. 11: 2707-2744 (2010) - [c16]Raphaël Bailly, Amaury Habrard, François Denis:
A Spectral Approach for Probabilistic Grammatical Inference on Trees. ALT 2010: 74-88
2000 – 2009
- 2009
- [c15]Laurent Boyer, Olivier Gandrillon, Amaury Habrard, Mathilde Pellerin, Marc Sebban:
Learning Constrained Edit State Machines. ICTAI 2009: 734-741 - 2008
- [j2]Marc Bernard, Laurent Boyer, Amaury Habrard, Marc Sebban:
Learning probabilistic models of tree edit distance. Pattern Recognit. 41(8): 2611-2629 (2008) - [c14]Alexander Clark, Rémi Eyraud, Amaury Habrard:
A Polynomial Algorithm for the Inference of Context Free Languages. ICGI 2008: 29-42 - [c13]François Denis, Édouard Gilbert, Amaury Habrard, Faissal Ouardi, Marc Tommasi:
Relevant Representations for the Inference of Rational Stochastic Tree Languages. ICGI 2008: 57-70 - [c12]Laurent Boyer, Yann Esposito, Amaury Habrard, José Oncina, Marc Sebban:
SEDiL: Software for Edit Distance Learning. ECML/PKDD (2) 2008: 672-677 - [c11]Amaury Habrard, José Manuel Iñesta Quereda, David Rizo, Marc Sebban:
Melody Recognition with Learned Edit Distances. SSPR/SPR 2008: 86-96 - [i3]François Denis, Amaury Habrard, Rémi Gilleron, Marc Tommasi, Édouard Gilbert:
On Probability Distributions for Trees: Representations, Inference and Learning. CoRR abs/0807.2983 (2008) - 2007
- [c10]François Denis, Amaury Habrard:
Learning Rational Stochastic Tree Languages. ALT 2007: 242-256 - [c9]Laurent Boyer, Amaury Habrard, Marc Sebban:
Learning Metrics Between Tree Structured Data: Application to Image Recognition. ECML 2007: 54-66 - 2006
- [c8]François Denis, Yann Esposito, Amaury Habrard:
Learning Rational Stochastic Languages. COLT 2006: 274-288 - [c7]Marc Bernard, Amaury Habrard, Marc Sebban:
Learning Stochastic Tree Edit Distance. ECML 2006: 42-53 - [c6]Amaury Habrard, François Denis, Yann Esposito:
Using Pseudo-stochastic Rational Languages in Probabilistic Grammatical Inference. ICGI 2006: 112-124 - [c5]Amaury Habrard, José Oncina:
Learning Multiplicity Tree Automata. ICGI 2006: 268-280 - [i2]François Denis, Yann Esposito, Amaury Habrard:
Learning rational stochastic languages. CoRR abs/cs/0602062 (2006) - [i1]Amaury Habrard, François Denis, Yann Esposito:
Using Pseudo-Stochastic Rational Languages in Probabilistic Grammatical Inference. CoRR abs/cs/0607085 (2006) - 2005
- [j1]Amaury Habrard, Marc Bernard, Marc Sebban:
Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data. Fundam. Informaticae 66(1-2): 103-130 (2005) - [c4]Amaury Habrard, Marc Bernard, Marc Sebban:
Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms. FLAIRS 2005: 493-498 - 2003
- [c3]Amaury Habrard, Marc Bernard, François Jacquenet:
Multi-relational Data Mining in Medical Databases. AIME 2003: 365-374 - [c2]Amaury Habrard, Marc Bernard, Marc Sebban:
Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference. ECML 2003: 169-180 - 2002
- [c1]Amaury Habrard, Marc Bernard, François Jacquenet:
Generalized Stochastic Tree Automata for Multi-relational Data Mining. ICGI 2002: 120-133
Coauthor Index
[c68] [c67] [c64] [c63] [j26] [j25] [j24] [j22] [j16] [c53] [c52] [i28] [j14] [j13] [c50] [c49] [c48] [c46] [i21] [i20] [i19] [j12] [c45] [c44] [c43] [c40] [c39] [p1] [j11] [j9] [i15] [i14] [b1] [c35] [c33] [c32] [j7] [c31] [i9] [j6] [c27] [c25] [i8] [j4] [c24] [c23] [c21] [c20] [c19] [c15] [j2] [c12] [c11] [c9] [c7] [j1] [c4] [c2]
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