Generalized Stochastic Tree Automata for Multi-relational Data Mining. - Amaury Habrard; Marc Bernard; François Jacquenet (ICGI, 2002)

Multi-relational Data Mining in Medical Databases. - Amaury Habrard; Marc Bernard; François Jacquenet (AIME, 2003)

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

Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms. - Amaury Habrard; Marc Bernard; Marc Sebban (FLAIRS, 2005)

Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data. - Amaury Habrard; Marc Bernard; Marc Sebban (Fundam. Informaticae, 2005)

Learning Rational Stochastic Languages. - François Denis; Yann Esposito; Amaury Habrard (COLT, 2006)

Learning Stochastic Tree Edit Distance. - Marc Bernard; Amaury Habrard; Marc Sebban (ECML, 2006)

Using Pseudo-stochastic Rational Languages in Probabilistic Grammatical Inference. - Amaury Habrard; François Denis; Yann Esposito (ICGI, 2006)

Learning Multiplicity Tree Automata. - Amaury Habrard; José Oncina (ICGI, 2006)

Learning Rational Stochastic Tree Languages. - François Denis; Amaury Habrard (ALT, 2007)

Learning Metrics Between Tree Structured Data: Application to Image Recognition. - Laurent Boyer 002; Amaury Habrard; Marc Sebban (ECML, 2007)

A Polynomial Algorithm for the Inference of Context Free Languages. - Alexander Clark; Rémi Eyraud; Amaury Habrard (ICGI, 2008)

Relevant Representations for the Inference of Rational Stochastic Tree Languages. - François Denis; Édouard Gilbert; Amaury Habrard; Faissal Ouardi; Marc Tommasi (ICGI, 2008)

SEDiL: Software for Edit Distance Learning. - Laurent Boyer 002; Yann Esposito; Amaury Habrard; José Oncina; Marc Sebban (ECML/PKDD (2), 2008)

Melody Recognition with Learned Edit Distances. - Amaury Habrard; José Manuel Iñesta Quereda; David Rizo; Marc Sebban (SSPR/SPR, 2008)

On Probability Distributions for Trees: Representations, Inference and Learning - François Denis; Amaury Habrard; Rémi Gilleron; Marc Tommasi; Édouard Gilbert (CoRR, 2008)

Learning probabilistic models of tree edit distance. - Marc Bernard; Laurent Boyer 002; Amaury Habrard; Marc Sebban (Pattern Recognit., 2008)

Learning Constrained Edit State Machines. - Laurent Boyer 002; Olivier Gandrillon; Amaury Habrard; Mathilde Pellerin; Marc Sebban (ICTAI, 2009)

A Spectral Approach for Probabilistic Grammatical Inference on Trees. - Raphaël Bailly; Amaury Habrard; François Denis (ALT, 2010)

Using Contextual Representations to Efficiently Learn Context-Free Languages. - Alexander Clark; Rémi Eyraud; Amaury Habrard (J. Mach. Learn. Res., 2010)

Sparse Domain Adaptation in Projection Spaces Based on Good Similarity Functions. - Emilie Morvant; Amaury Habrard; Stéphane Ayache (ICDM, 2011)

An Experimental Study on Learning with Good Edit Similarity Functions. - Aurélien Bellet; Marc Sebban; Amaury Habrard (ICTAI, 2011)

Domain Adaptation with Good Edit Similarities: A Sparse Way to Deal with Scaling and Rotation Problems in Image Classification. - Amaury Habrard; Jean-Philippe Peyrache; Marc Sebban (ICTAI, 2011)

Learning Good Edit Similarities with Generalization Guarantees. - Aurélien Bellet; Amaury Habrard; Marc Sebban (ECML/PKDD (1), 2011)

On the Usefulness of Similarity Based Projection Spaces for Transfer Learning. - Emilie Morvant; Amaury Habrard; Stéphane Ayache (SIMBAD, 2011)

VideoSense at TRECVID 2011: Semantic Indexing from Light Similarity Functions-based Domain Adaptation with Stacking. - Emilie Morvant; Stéphane Ayache; Amaury Habrard; Miriam Redi; Claudiu Tanase; Bernard Mérialdo; Bahjat Safadi; Franck Thollard; Nadia Derbas; Georges Quénot (TRECVID, 2011)

Similarity Learning for Provably Accurate Sparse Linear Classification. - Aurélien Bellet; Amaury Habrard; Marc Sebban (ICML, 2012)

PAC-Bayesian Majority Vote for Late Classifier Fusion - Emilie Morvant; Amaury Habrard; Stéphane Ayache (CoRR, 2012)

PAC-Bayesian Learning and Domain Adaptation - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (CoRR, 2012)

Speeding Up Syntactic Learning Using Contextual Information. - Leonor Becerra-Bonache; Élisa Fromont; Amaury Habrard; Michaël Perrot; Marc Sebban (ICGI, 2012)

Parsimonious unsupervised and semi-supervised domain adaptation with good similarity functions. - Emilie Morvant; Amaury Habrard; Stéphane Ayache (Knowl. Inf. Syst., 2012)

Good edit similarity learning by loss minimization. - Aurélien Bellet; Amaury Habrard; Marc Sebban (Mach. Learn., 2012)

Unsupervised Visual Domain Adaptation Using Subspace Alignment. - Basura Fernando; Amaury Habrard; Marc Sebban; Tinne Tuytelaars (ICCV, 2013)

A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (ICML (3), 2013)

Boosting for Unsupervised Domain Adaptation. - Amaury Habrard; Jean-Philippe Peyrache; Marc Sebban (ECML/PKDD (2), 2013)

A Survey on Metric Learning for Feature Vectors and Structured Data. - Aurélien Bellet; Amaury Habrard; Marc Sebban (CoRR, 2013)

Iterative Self-Labeling Domain Adaptation for Linear Structured Image Classification. - Amaury Habrard; Jean-Philippe Peyrache; Marc Sebban (Int. J. Artif. Intell. Tools, 2013)

Modeling Perceptual Color Differences by Local Metric Learning. - Michaël Perrot; Amaury Habrard; Damien Muselet; Marc Sebban (ECCV (5), 2014)

Some improvements of the spectral learning approach for probabilistic grammatical inference. - Mattias Gybels; François Denis; Amaury Habrard (ICGI, 2014)

Majority Vote of Diverse Classifiers for Late Fusion. - Emilie Morvant; Amaury Habrard; Stéphane Ayache (S+SSPR, 2014)

Subspace Alignment For Domain Adaptation. - Basura Fernando; Amaury Habrard; Marc Sebban; Tinne Tuytelaars (CoRR, 2014)

Learning a priori constrained weighted majority votes. - Aurélien Bellet; Amaury Habrard; Emilie Morvant; Marc Sebban (Mach. Learn., 2014)

A Theoretical Analysis of Metric Hypothesis Transfer Learning. - Michaël Perrot; Amaury Habrard (ICML, 2015)

Regressive Virtual Metric Learning. - Michaël Perrot; Amaury Habrard (NIPS, 2015)

Joint Semi-supervised Similarity Learning for Linear Classification. - Maria-Irina Nicolae; Éric Gaussier; Amaury Habrard; Marc Sebban (ECML/PKDD (1), 2015)

An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (CoRR, 2015)

PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (CoRR, 2015)

Algorithmic Robustness for Semi-Supervised (ε, γ, τ)-Good Metric Learning. - Maria-Irina Nicolae; Marc Sebban; Amaury Habrard; Éric Gaussier; Massih-Reza Amini (ICLR (Workshop), 2015)

Robustness and generalization for metric learning. - Aurélien Bellet; Amaury Habrard (Neurocomputing, 2015)

Metric Learning - Aurélien Bellet; Amaury Habrard; Marc Sebban (Morgan & Claypool Publishers, 2015)

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

Mapping Estimation for Discrete Optimal Transport. - Michaël Perrot; Nicolas Courty; Rémi Flamary; Amaury Habrard (NIPS, 2016)

Similarity Learning for Time Series Classification. - Maria-Irina Nicolae; Éric Gaussier; Amaury Habrard; Marc Sebban (CoRR, 2016)

Learning discriminative tree edit similarities for linear classification - Application to melody recognition. - Aurélien Bellet; José Francisco Bernabeu; Amaury Habrard; Marc Sebban (Neurocomputing, 2016)

Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning. - François Denis; Mattias Gybels; Amaury Habrard (J. Mach. Learn. Res., 2016)

A new boosting algorithm for provably accurate unsupervised domain adaptation. - Amaury Habrard; Jean-Philippe Peyrache; Marc Sebban (Knowl. Inf. Syst., 2016)

Dict2vec : Learning Word Embeddings using Lexical Dictionaries. - Julien Tissier; Christophe Gravier; Amaury Habrard (EMNLP, 2017)

Joint distribution optimal transportation for domain adaptation. - Nicolas Courty; Rémi Flamary; Amaury Habrard; Alain Rakotomamonjy (NIPS, 2017)

Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection. - Jordan Fréry; Amaury Habrard; Marc Sebban; Olivier Caelen; Liyun He-Guelton (ECML/PKDD (1), 2017)

Theoretical Analysis of Domain Adaptation with Optimal Transport. - Ievgen Redko; Amaury Habrard; Marc Sebban (ECML/PKDD (2), 2017)

Unsupervised Domain Adaptation Based on Subspace Alignment. - Basura Fernando; Rahaf Aljundi; Rémi Emonet; Amaury Habrard; Marc Sebban; Tinne Tuytelaars (Domain Adaptation in Computer Vision Applications, 2017)

Online Non-linear Gradient Boosting in Multi-latent Spaces. - Jordan Fréry; Amaury Habrard; Marc Sebban; Olivier Caelen; Liyun He-Guelton (IDA, 2018)

Tree-Based Cost Sensitive Methods for Fraud Detection in Imbalanced Data. - Guillaume Metzler; Xavier Badiche; Brahim Belkasmi; Élisa Fromont; Amaury Habrard; Marc Sebban (IDA, 2018)

Non-Linear Gradient Boosting for Class-Imbalance Learning. - Jordan Fréry; Amaury Habrard; Marc Sebban; Liyun He-Guelton (LIDTA@ECML/PKDD, 2018)

Learning maximum excluding ellipsoids from imbalanced data with theoretical guarantees. - Guillaume Metzler; Xavier Badiche; Brahim Belkasmi; Élisa Fromont; Amaury Habrard; Marc Sebban (Pattern Recognit. Lett., 2018)

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

From Cost-Sensitive to Tight F-measure Bounds. - Kevin Bascol; Rémi Emonet; Élisa Fromont; Amaury Habrard; Guillaume Metzler; Marc Sebban (AISTATS, 2019)

Anomaly Detection, Consider Your Dataset First An Illustration on Fraud Detection. - Ayman Alazizi; Amaury Habrard; François Jacquenet; Liyun He-Guelton; Frédéric Oblé; Wissam Siblini (ICTAI, 2019)

Metric Learning from Imbalanced Data. - Léo Gautheron; Amaury Habrard; Emilie Morvant; Marc Sebban (ICTAI, 2019)

An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data. - Rémi Viola; Rémi Emonet; Amaury Habrard; Guillaume Metzler; Sébastien Riou; Marc Sebban (ICTAI, 2019)

Differentially Private Optimal Transport: Application to Domain Adaptation. - Nam Lê Tien; Amaury Habrard; Marc Sebban (IJCAI, 2019)

Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting. - Léo Gautheron; Pascal Germain; Amaury Habrard; Emilie Morvant; Marc Sebban; Valentina Zantedeschi (CoRR, 2019)

On the analysis of adaptability in multi-source domain adaptation. - Ievgen Redko; Amaury Habrard; Marc Sebban (Mach. Learn., 2019)

Deep multi-Wasserstein unsupervised domain adaptation. - Tien-Nam Le; Amaury Habrard; Marc Sebban (Pattern Recognit. Lett., 2019)

Online Performance Evaluation of Deep Learning Networks for Profiled Side-Channel Analysis. - Damien Robissout; Gabriel Zaid; Brice Colombier; Lilian Bossuet; Amaury Habrard (COSADE, 2020)

Dual Sequential Variational Autoencoders for Fraud Detection. - Ayman Alazizi; Amaury Habrard; François Jacquenet; Liyun He-Guelton; Frédéric Oblé (IDA, 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, 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 (ECML/PKDD (3), 2020)

Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening. - Louis Béthune; Yacouba Kaloga; Pierre Borgnat; Aurélien Garivier; Amaury Habrard (Algorithms, 2020)

A survey on domain adaptation theory. - Ievgen Redko; Emilie Morvant; Amaury Habrard; Marc Sebban; Younès Bennani (CoRR, 2020)

Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms. - Quentin Bouniot; Ievgen Redko; Romaric Audigier; Angélique Loesch; Amaury Habrard (CoRR, 2020)

Online Performance Evaluation of Deep Learning Networks for Side-Channel Analysis. - Damien Robissout; Gabriel Zaid; Brice Colombier; Lilian Bossuet; Amaury Habrard (IACR Cryptol. ePrint Arch., 2020)

Understanding Methodology for Efficient CNN Architectures in Profiling Attacks. - Gabriel Zaid; Lilian Bossuet; Amaury Habrard; Alexandre Venelli (IACR Cryptol. ePrint Arch., 2020)

PAC-Bayes and domain adaptation. - Pascal Germain; Amaury Habrard; François Laviolette; Emilie Morvant (Neurocomputing, 2020)

Metric Learning from Imbalanced Data with Generalization Guarantees. - Léo Gautheron; Amaury Habrard; Emilie Morvant; Marc Sebban (Pattern Recognit. Lett., 2020)

Methodology for Efficient CNN Architectures in Profiling Attacks. - Gabriel Zaid; Lilian Bossuet; Amaury Habrard; Alexandre Venelli (IACR Trans. Cryptogr. Hardw. Embed. Syst., 2020)

Multiview Variational Graph Autoencoders for Canonical Correlation Analysis. - Yacouba Kaloga; Pierre Borgnat; Sundeep Prabhakar Chepuri; Patrice Abry; Amaury Habrard (ICASSP, 2021)

A PAC-Bayes Analysis of Adversarial Robustness. - Paul Viallard; 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 (NeurIPS, 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 (2), 2021)

A General Framework for the Derandomization of PAC-Bayesian Bounds. - Paul Viallard; Pascal Germain; Amaury Habrard; Emilie Morvant (CoRR, 2021)

A Nearest Neighbor Algorithm for Imbalanced Classification. - Rémi Viola; Rémi Emonet; Amaury Habrard; Guillaume Metzler; Sébastien Riou; Marc Sebban (Int. J. Artif. Intell. Tools, 2021)

Improving Deep Learning Networks for Profiled Side-channel Analysis Using Performance Improvement Techniques. - Damien Robissout; Lilian Bossuet; Amaury Habrard; Vincent Grosso (ACM J. Emerg. Technol. Comput. Syst., 2021)

Iterative multilinear optimization for planar model fitting under geometric constraints. - Jorge Azorín López; Marc Sebban; Andrés Fuster Guilló; Marcelo Saval-Calvo; Amaury Habrard (PeerJ Comput. Sci., 2021)

Variational graph autoencoders for multiview canonical correlation analysis. - Yacouba Kaloga; Pierre Borgnat; Sundeep Prabhakar Chepuri; Patrice Abry; Amaury Habrard (Signal Process., 2021)

Ranking Loss: Maximizing the Success Rate in Deep Learning Side-Channel Analysis. - Gabriel Zaid; Lilian Bossuet; François Dassance; Amaury Habrard; Alexandre Venelli (IACR Trans. Cryptogr. Hardw. Embed. Syst., 2021)

Efficiency through Diversity in Ensemble Models applied to Side-Channel Attacks - A Case Study on Public-Key Algorithms -. - Gabriel Zaid; Lilian Bossuet; Amaury Habrard; Alexandre Venelli (IACR Trans. Cryptogr. Hardw. Embed. Syst., 2021)

Improving Few-Shot Learning Through Multi-task Representation Learning Theory. - Quentin Bouniot; Ievgen Redko; Romaric Audigier; Angélique Loesch; Amaury Habrard (ECCV (20), 2022)

A Simple Way to Learn Metrics Between Attributed Graphs. - Yacouba Kaloga; Pierre Borgnat; Amaury Habrard (LoG, 2022)

Learning PDE to Model Self-Organization of Matter. - Eduardo Brandao; Jean-Philippe Colombier; Stefan Duffner; Rémi Emonet; Florence Garrelie; Amaury Habrard; François Jacquenet; Anthony Nakhoul; Marc Sebban (Entropy, 2022)

Conditional Variational AutoEncoder based on Stochastic Attack. - Gabriel Zaid; Lilian Bossuet; Mathieu Carbone; Amaury Habrard; Alexandre Venelli (IACR Cryptol. ePrint Arch., 2022)

MetaAP: A meta-tree-based ranking algorithm optimizing the average precision from imbalanced data. - Rémi Viola; Léo Gautheron; Amaury Habrard; Marc Sebban (Pattern Recognit. Lett., 2022)

Proposal-Contrastive Pretraining for Object Detection from Fewer Data. - Quentin Bouniot; Romaric Audigier; Angélique Loesch; Amaury Habrard (ICLR, 2023)

Is My Neural Net Driven by the MDL Principle? - Eduardo Brandao; Stefan Duffner; Rémi Emonet; Amaury Habrard; François Jacquenet; Marc Sebban (ECML/PKDD (2), 2023)

Predictive Modeling of Body Shape Changes in Individuals on Dietetic Treatment Using Recurrent Networks. - Nahuel E. Garcia-D'Urso; Pablo Ramon Guevara; Jorge Azorín López; Marc Sebban; Amaury Habrard; Andrés Fuster Guilló (UCAmI (2), 2023)

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

Conditional Variational AutoEncoder based on Stochastic Attacks. - Gabriel Zaid; Lilian Bossuet; Mathieu Carbone; Amaury Habrard; Alexandre Venelli (IACR Trans. Cryptogr. Hardw. Embed. Syst., 2023)

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 (AISTATS, 2024)

Generative shape deformation with optimal transport using learned transformations. - Jorge Azorín López; Marc Sebban; Nahuel E. Garcia-D'Urso; Amaury Habrard; Andrés Fuster Guilló (IJCNN, 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 (ECML/PKDD (4), 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 (4), 2024)

Advanced Traffic Engineering in WAN Using Graph Attention Networks. - Sami Marouani; Kamal Singh; Baptiste Jeudy; Abbas Bradai; Amaury Habrard (WiMob, 2024)

On the theoretical limit of gradient descent for Simple Recurrent Neural Networks with finite precision. - Volodimir Mitarchuk; Rémi Emonet; Rémi Eyraud; Amaury Habrard (Trans. Mach. Learn. Res., 2024)

A general framework for the practical disintegration of PAC-Bayesian bounds. - Paul Viallard; Pascal Germain; Amaury Habrard; Emilie Morvant (Mach. Learn., 2024)

Scoring the predictions: a way to improve profiling side-channel attacks. - Damien Robissout; Lilian Bossuet; Amaury Habrard (J. Cryptogr. Eng., 2024)

Provably accurate adaptive sampling for collocation points in physics-informed neural networks. - Antoine Caradot; Rémi Emonet; Amaury Habrard; Abdel-Rahim Mezidi; Marc Sebban (CoRR, 2025)

Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks. - Kamal Singh; Sami Marouani; Ahmad Al Sheikh; Pham Tran Anh Quang; Amaury Habrard (CoRR, 2025)

Méthode de quadrature pour les PINNs fondée théoriquement sur la hessienne des résiduels. - Antoine Caradot; Rémi Emonet; Amaury Habrard; Abdel-Rahim Mezidi; Marc Sebban (CoRR, 2025)