Nombre de documents

77


Article dans une revue11 documents

  • Aurélien Bellet, José Bernabeu, Amaury Habrard, Marc Sebban. Learning Discriminative Tree Edit Similarities for Linear Classification - Application to Melody Recognition. Neurocomputing, Elsevier, 2016, 214, pp.155-161. 〈10.1016/j.neucom.2016.06.006〉. 〈hal-01330492〉
  • François Denis, Mattias Gybels, Amaury Habrard. Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning. Journal of Machine Learning Research, Journal of Machine Learning Research, 2016, 17 (31), pp.1-32. 〈http://jmlr.org/papers/v17/14-501.html〉. 〈hal-01306915〉
  • Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban. A New Boosting Algorithm for Provably Accurate Unsupervised Domain Adaptation. Knowledge and Information Systems (KAIS), Springer, 2015, pp.1. 〈10.1007/s10115-015-0839-2〉. 〈hal-01144896〉
  • Aurélien Bellet, Amaury Habrard. Robustness and Generalization for Metric Learning. Neurocomputing, Elsevier, 2015, pp.16. 〈10.1016/j.neucom.2014.09.044〉. 〈hal-01075370〉
  • Aurélien Bellet, Amaury Habrard, Emilie Morvant, Marc Sebban. Learning A Priori Constrained Weighted Majority Votes. Machine Learning, Springer Verlag, 2014, 97 (1-2), pp.129-154. 〈10.1007/s10994-014-5462-z〉. 〈hal-01009578〉
  • Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban. Iterative Self-labeling Domain Adaptation for Linear Structured Image Classification. International Journal on Artificial Intelligence Tools, World Scientific Publishing, 2013. 〈hal-00869404〉
  • Emilie Morvant, Amaury Habrard, Stéphane Ayache. Parsimonious Unsupervised and Semi-Supervised Domain Adaptation with Good Similarity Functions. Knowledge and Information Systems (KAIS), Springer, 2012, 33 (2), pp.309-349. 〈10.1007/s10115-012-0516-7〉. 〈hal-00686205〉
  • Aurélien Bellet, Amaury Habrard, Marc Sebban. Good edit similarity learning by loss minimization. Machine Learning, Springer Verlag, 2012, pp.5-35. 〈10.1007/s10994-012-5293-8〉. 〈hal-00690240〉
  • Alexander Clark, Rémi Eyraud, Amaury Habrard. Using Contextual Representations to Efficiently Learn Context-Free Languages. Journal of Machine Learning Research, Journal of Machine Learning Research, 2010, 11, pp.2707-2744. 〈hal-00607098〉
  • Marc Bernard, Laurent Boyer, Amaury Habrard, Marc Sebban. Learning Probabilistic Models of Tree Edit Distance. Pattern Recognition, Elsevier, 2008, 41 (8), pp.2611-2629. 〈10.1016/j.patcog.2008.01.011〉. 〈hal-00224992〉
  • Amaury Habrard, Marc Bernard, Marc Sebban. Detecting Irrelevant subtrees to improve probabilistic learning from tree-structured data. Fundamenta Informaticae, Polskie Towarzystwo Matematyczne, 2005, 66 (1,2), pp.103-130. 〈hal-00369445〉

Communication dans un congrès56 documents

  • Julien Tissier, Christophe Gravier, Amaury Habrard. Dict2vec : Learning Word Embeddings using Lexical Dictionaries. Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), Sep 2017, Copenhague, Denmark. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp.254-263, 〈http://emnlp2017.net/〉. 〈ujm-01613953〉
  • Jordan Frery, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton. Efficient top rank optimization with gradient boosting for supervised anomaly detection. ECML PKDD 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2017, Skopje, Macedonia. 2017. 〈hal-01611346v2〉
  • Jordan Frery, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton. Efficient top rank optimization with gradient boosting for supervised anomaly detection. ECML-PKDD 2017, Sep 2017, Skopje, Macedonia. 〈hal-01613561〉
  • Ievgen Redko, Amaury Habrard, Marc Sebban. Theoretical Analysis of Domain Adaptation with Optimal Transport. ECML PKDD 2017, Sep 2017, Skopje, Macedonia. 〈hal-01613564〉
  • Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy. Joint distribution optimal transportation for domain adaptation. NIPS 2017, Dec 2017, Los Angeles, United States. 〈hal-01620589〉
  • Guillaume Metzler, Xavier Badiche, Brahim Belkasmi, Stéphane Canu, Elisa Fromont, et al.. Apprentissage de sphères maximales d’exclusion avec garanties théoriques. Conférence sur l'Apprentissage Automatique, Jun 2017, Grenoble, France. 2017, 〈http://cap2017.imag.fr〉. 〈hal-01581550〉
  • Michaël Perrot, Amaury Habrard. Bornes en Généralisation à Convergence Rapide pour le Transfert d'Hypothèses en Apprentissage de Métriques. Conférence francophone sur l'Apprentissage Automatique, Jul 2016, Marseille, France. 〈hal-01382217〉
  • Michaël Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard. Mapping Estimation for Discrete Optimal Transport. Neural Information Processing System, Dec 2016, Barcelone, Spain. Advances in Neural Information Processing Systems 29. 〈hal-01376970〉
  • Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. A New PAC-Bayesian Perspective on Domain Adaptation. 33rd International Conference on Machine Learning (ICML 2016), Jun 2016, New York, NY, United States. 2016, Proceedings of the 33rd International Conference on Machine Learning. 〈hal-01307045〉
  • Nicolae Irina, Marc Sebban, Amaury Habrard, Eric Gaussier, Massih-Reza Amini. Algorithmic Robustness for Semi-Supervised (ε, γ, τ )-Good Metric Learning. International Conference on Neural Information Processing ICONIP, Nov 2015, Istanbul, Turkey. pp.10, 2015. 〈hal-01223411〉
  • Pascal Germain, François Laviolette, Amaury Habrard, Emilie Morvant. A New PAC-Bayesian View of Domain Adaptation. NIPS 2015 Workshop on Transfer and Multi-Task Learning: Trends and New Perspectives, Dec 2015, Montréal, Canada. 〈hal-01223164〉
  • Michaël Perrot, Amaury Habrard. A Theoretical Analysis of Metric Hypothesis Transfer Learning. International Conference on Machine Learning, Jul 2015, Lille, France. 〈hal-01175610〉
  • Nicolae Irina, Gaussier Eric, Amaury Habrard, Marc Sebban. Joint Semi-supervised Similarity Learning for Linear Classification. ECML-PKDD 2015, Sep 2015, Porto, Portugal. 〈10.1007/978-3-319-23528-8 37〉. 〈hal-01204642〉
  • Michaël Perrot, Amaury Habrard. Regressive Virtual Metric Learning. Neural Information Processing Systems, Dec 2015, Montréal, Canada. 28, Advances in Neural Information Processing Systems. 〈hal-01220665〉
  • Mattias Gybels, Francois Denis, Amaury Habrard. Some improvements of the spectral learning approach for probabilistic grammatical inference. Proceedings of the 12th International Conference on Grammatical Inference (ICGI), Sep 2014, Kyoto, Japan. 34, pp.64-78, 2014. 〈hal-01075979〉
  • Michaël Perrot, Amaury Habrard, Damien Muselet, Marc Sebban. Modeling Perceptual Color Differences by Local Metric Learning. European Conference on Computer Vision, Sep 2014, Zurich, Switzerland. 2014. 〈hal-01009610〉
  • Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context. NIPS 2014 Workshop on Transfer and Multi-task learning: Theory Meets Practice, Dec 2014, Montréal, Canada. 2014. 〈hal-01093565v2〉
  • Francois Denis, Mattias Gybels, Amaury Habrard. Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning. The International Conference on Machine Learning (ICML), Jun 2014, China. pp.JMLR: W&CP volume 32, 2014. 〈hal-01009395〉
  • Emilie Morvant, Amaury Habrard, Stéphane Ayache. Majority Vote of Diverse Classifiers for Late Fusion. IAPR Joint International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recignition, Aug 2014, Joensuu, Finland. pp.20, 2014. 〈hal-00985839v2〉
  • Michaël Perrot, Amaury Habrard, Damien Muselet, Marc Sebban. Modélisation de Distances Couleur Uniformes par Apprentissage de Métriques Locales. CAp'2014 : Conférence d'Apprentissage Automatique, Jul 2014, Saint-Étienne, France. 2014. 〈hal-01016472〉
  • Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers. International Conference on Machine Learning 2013, Jun 2013, Atlanta, United States. pp.738-746, 2013. 〈hal-00822685〉
  • Aurélien Bellet, Amaury Habrard, Emilie Morvant, Marc Sebban. Vote de majorité a priori contraint pour la classification binaire : spécification au cas des plus proches voisins. Conférence sur l'Apprentissage Automatique (CAp), Jul 2013, Lille, France. 2013. 〈hal-00850241〉
  • Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. Une analyse PAC-Bayésienne de l'adaptation de domaine et sa spécialisation aux classifieurs linéaires. Conférence sur l'apprentissage automatique, Jul 2013, Villeneuve d'Ascq, France. pp.3, 2013. 〈hal-00850242〉
  • Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars. Unsupervised Visual Domain Adaptation Using Subspace Alignment. ICCV 2013, Dec 2013, Sydney, Australia. pp.2960-2967, 2013. 〈hal-00869417〉
  • Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban. Boosting for Unsupervised Domain Adaptation. ECML PKDD 2013, Sep 2013, Prague, Czech Republic. pp.433-448, 2013. 〈hal-00869394〉
  • Mattias Gybels, François Denis, Amaury Habrard. Utilisation de matrices de Hankel non bornées pour l'apprentissage spectral de langages stochastiques. Conférence d'Apprentissage, 2013, France. 2013. 〈ujm-00870081〉
  • Leonor Becerra-Bonache, Elisa Fromont, Amaury Habrard, Michael Perrot, Marc Sebban. Speeding Up Syntactic Learning Using Contextual Information. International Conference on Grammatical Inference, Sep 2012, United States. 21, pp.49-53, 2012. 〈hal-00717809〉
  • Marc Bernard, Elisa Fromont, Amaury Habrard, Marc Sebban. Handwritten Digit Recognition using Edit Distance-Based KNN. Teaching Machine Learning Workshop, Jun 2012, Edinburgh, Scotland, United Kingdom. 〈hal-00714509〉
  • Aurélien Bellet, Amaury Habrard, Marc Sebban. Similarity Learning for Provably Accurate Sparse Linear Classification. International Conference on Machine Learning, Jun 2012, United Kingdom. 2012. 〈hal-00708401〉
  • Emilie Morvant, Amaury Habrard, Stéphane Ayache. Étude de la généralisation de DASF à l'adaptation de domaine semi-supervisée. Laurent Bougrain. Conférence Francophone sur l'Apprentissage Automatique - CAp 2012, May 2012, Nancy, France. pp.111-126, 2012. 〈hal-00685524〉
  • Aurélien Bellet, Amaury Habrard, Marc Sebban. Apprentissage de bonnes similarités pour la classification linéaire parcimonieuse. Laurent Bougrain. Conférence Francophone sur l'Apprentissage Automatique - CAp 2012, May 2012, Nancy, France. 16 p., 2012. 〈hal-00690242〉
  • Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban. Un Cadre Formel de Boosting pour l'Adaptation de Domaine. CAP 2012, May 2012, Nancy, France. 2012. 〈hal-00690243〉
  • Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. PAC-Bayesian Learning and Domain Adaptation. Multi-Trade-offs in Machine Learning, NIPS 2012 Workshop, Dec 2012, Lake Tahoe, United States. 〈hal-00749366〉
  • Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban. Un Cadre Formel de Boosting pour l'Adaptation de Domaine. Laurent Bougrain. Conférence Francophone sur l'Apprentissage Automatique - CAp 2012, May 2012, Nancy, France. 16 p., 2012, Actes de la Conférence Francophone sur l'Apprentissage Automatique - CAp 2012. 〈hal-00745487〉
  • Emilie Morvant, Stéphane Ayache, Amaury Habrard. Adaptation de domaine parcimonieuse par pondération de bonnes fonctions de similarité. Presses de L'université des Antilles et de la Guyanne. Conférence Francophone d'Apprentissage (CAp), May 2011, Chambéry, France. Publibook, pp.295-310, 2011, Sciences exactes et naturelles. 〈hal-00630300〉
  • Emilie Morvant, Amaury Habrard, Stéphane Ayache. Sparse Domain Adaptation in Projection Spaces based on Good Similarity Functions. Diane Cook, Jian Pei, Wei Wang, Osmar Zaïane, and Xindong Wu. IEEE International Conference on Data Mining series (ICDM), Dec 2011, Vancouver, Canada. IEEE Computer Society, pp.457-466, 2011. 〈hal-00629207〉
  • Emilie Morvant, Amaury Habrard, Stéphane Ayache. On the Usefulness of Similarity Based Projection Spaces for Transfer Learning. Marcello Pelillo and Edwin R. Hancock. First International Workshop on Similarity-Based Pattern Recognition, Sep 2011, Venise, Italy. Springer, 7005, pp.1-16, 2011, LNCS. 〈hal-00628991〉
  • Emilie Morvant, Amaury Habrard, Stéphane Ayache. Sparse Domain Adaptation in a Good Similarity-Based Projection Space. Workshop at NIPS 2011: Domain Adaptation Workshop: Theory and Application, Dec 2011, Grenade, Spain. 〈hal-00654227〉
  • Aurélien Bellet, Amaury Habrard, Marc Sebban. Learning Good Edit Similarities with Generalization Guarantees. European Conference on Machine Learning, Sep 2011, Athens, Greece. pp.188-203, 2011. 〈hal-00608631〉
  • Aurélien Bellet, Amaury Habrard, Marc Sebban. An Experimental Study on Learning with Good Edit Similarity Functions. ICTAI 2011, Nov 2011, United States. 2011. 〈hal-00618706〉
  • 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, Nov 2011, United States. 2011. 〈hal-00618757〉
  • Emilie Morvant, Stéphane Ayache, Amaury Habrard, Miriam Redi, Claudiu Tanase, et al.. VideoSense at TRECVID 2011 : Semantic Indexing from Light Similarity Functions-based Domain Adaptation with Stacking. TRECVID 2011 - TREC Video Retrieval Evaluation workshop, Nov 2011, Gaithersburg, MD, United States. NIST, 6p., 2011. 〈hal-00685530〉
  • Raphaël Bailly, Amaury Habrard, Francois Denis. A Spectral Approach for Probabilistic Grammatical Inference on Trees. 21st International Conference on Algorithmic Learning Theory (ALT 2010), Oct 2010, Australia. Springer, 6331, pp.74-88, 2010, Lecture Notes in Computer Science. 〈hal-00607096〉
  • Laurent Boyer, Olivier Gandrillon, Amaury Habrard, Mathilde Pellerin, Marc Sebban. Learning Constrained Edit State Machines. 21st IEEE International Conference on Tools with Artificial Intelligence, Nov 2009, United States. pp.734-741, 2009. 〈hal-00485560〉
  • Laurent Boyer, Yann Esposito, Amaury Habrard, Jose Oncina, Marc Sebban. SEDiL: Software for Edit Distance Learning. European Conference on Machine Learning (ECML 2008), Sep 2008, Belgium. Springer, pp.672-677, 2008, Lecture Notes in Computer Science. 〈hal-00295148〉
  • Amaury Habrard, Jose-Manuel Inesta, David Rizo, Marc Sebban. Melody Recognition with Learned Edit Distances. Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR 2008 and SPR 2008, Dec 2008, Orlando, United States. Springer, pp.86-96, 2008, Lecture Notes in Computer Science. 〈hal-00322432〉
  • François Denis, Edouard Gilbert, Amaury Habrard, Faïssal Ouardi, Marc Tommasi. Relevant Representations for the Inference of Rational Stochastic Tree Languages. Francois Coste and Alexander Clark and Laurent Miclet. International Colloquium on Grammatical Inference, 2008, St Malo, France. Springer Verlag, 5278, pp.57-70, 2008, Lecture Notes in Artificial Intelligence. 〈hal-00293511v3〉
  • Laurent Boyer, Amaury Habrard, Fabrice Muhlenbach, Marc Sebban. Learning String Edit Similarities using Constrained Finite State Machines. CAp'08, May 2008, Porquerolles, France. pp.37-52, 2008. 〈hal-00369444〉
  • Alexander Clark, Rémi Eyraud, Amaury Habrard. A Polynomial Algorithm for the Inference of Context Free Languages. Springer. 9th International Colloquium on Grammatical Inference - ICGI 2008, Sep 2008, St Malo, France. 5278, p.29-42, 2008, LNCS. 〈hal-00348673〉
  • François Denis, Amaury Habrard, Rémi Gilleron, Marc Tommasi, Édouard Gilbert. On Probability Distributions for Trees: Representations, Inference and Learning. NIPS Workshop on Representations and Inference on Probability Distributions, Dec 2007, Whistler, Canada. 2007. 〈inria-00294636〉
  • Francois Denis, Amaury Habrard. Learning rational stochastic tree languages. Springer. Proceedings of the 18th International Conference on Algorithmic Learning Theory (ALT'07), 2007, Japan. 4754, p.242-256, 2007, LNCS. 〈hal-00192401v2〉
  • Laurent Boyer, Amaury Habrard, Marc Sebban. Learning Metrics between Tree Structured Data: Application to Image Recognition. 18th European Conference on Machine Learning (ECML), Sep 2007, Warsaw, Poland. Springer, LNAI 4701, pp.54-66, 2007, Lecture Notes in Computer Science. 〈hal-00165954〉
  • Amaury Habrard, Jose Oncina. Learning Multipicity Tree Automata. Springer. ICGI 2006, 2006, TOkyo, Japan. p.268-280, 2006, LNCS 4201. 〈hal-00192410〉
  • Amaury Habrard, Francois Denis, Yann Esposito. Using Pseudo-Stochastic Rational Languages in Probabilistic Grammatical Inference. 8th International Colloquium on Grammatical Inference (ICGI'06), 2006, Japan. Springer, 4201, p.112-124, 2006, LNCS. 〈hal-00085176v2〉
  • Marc Bernard, Amaury Habrard, Marc Sebban. Learning Stochastic Tree Edit Distance. 17th European Conference on Machine Learning, Sep 2006, Berlin, Germany. pp.42-53, 2006, LNAI 4212. 〈ujm-00109696〉
  • Amaury Habrard, Marc Bernard, Marc Sebban. Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms. 18th International Florida Artificial Intelligence Research Society conference, May 2005, United States. pp.493-498, 2005. 〈ujm-00378062〉

Ouvrage (y compris édition critique et traduction)2 documents

Chapitre d'ouvrage1 document

  • Basura Fernando, Rahaf Aljundi, Rémi Emonet, Amaury Habrard, Marc Sebban, et al.. Unsupervised Domain Adaptation Based on Subspace Alignment. Domain Adaptation in Computer Vision Applications. Advances in Computer Vision and Pattern Recognition, 2017. 〈hal-01613051〉

Pré-publication, Document de travail4 documents

  • Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. PAC-Bayes and Domain Adaptation. 2017. 〈hal-01563152〉
  • François Denis, Mattias Gybels, Amaury Habrard. Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning. Extended version of a paper to appear at ICML 2014. 2013. 〈hal-00957470〉
  • Emilie Morvant, Amaury Habrard, Stéphane Ayache. Flexible Domain Adaptation for Multimedia Indexing. Best Poster Award. 2011. 〈hal-00634881〉
  • François Denis, Yann Esposito, Amaury Habrard. Learning rational stochastic languages. 15 pages. 2006. 〈hal-00019161〉

Rapport3 documents

  • Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers. [Research Report] Université Jean Monnet, Saint-Étienne (42); Département d'Informatique et de Génie Logiciel, Université Laval (Québec); ENS Paris; IST Austria. 2016. 〈hal-01134246v3〉
  • Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant. A New PAC-Bayesian Perspective on Domain Adaptation. [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. 〈hal-01163722v3〉
  • Emilie Morvant, Amaury Habrard, Stéphane Ayache. PAC-Bayesian Majority Vote for Late Classifier Fusion. [Research Report] LIF Marseille; LaHC Saint-Etienne. 2012. 〈hal-00714483〉