Publications
The list is outdated, check my DBLP and google scholar pages.-
NIPS'16
Mapping Estimation for Discrete Optimal Transport
Michael Perrot, Nicolas Courty, Rémi Flamary, Amaury Habrard.
Proceedings of Advances in Neural Information Processing Systems (NIPS), 2016. (to appear) -
ICML'16
A New PAC-Bayesian Perspective on Domain Adaptation
Pascal Germain, Amaury Habrard, Francois Laviolette and Emilie Morvant.
Proceedings of International Conference on Machine Learning (ICML), 2016.
(pdf, code) -
Neurocomputing
Learning Discriminative Tree Edit Similarities for Linear Classification - Application to Melody Recognition
Aurélien Bellet, Jose Francisco Bernabeu, Amaury Habrard and Marc Sebban.
Neurocomputing, 2016 (to appear). -
JMLR
Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning
Francois Denis, Mattias Gybels, Amaury Habrard
Journal of Machine Learning Research, vol 17(31):1-32, 2016.
(pdf, link with abstract) -
KAIS
A new boosting algorithm for provably accurate unsupervised domain adaptation
Amaury Habrard, Jean-Philippe Peyrache and Marc Sebban.
Knowledge and Information Systems, vol 47, n1, p.45-73, 2016.
(pdf). -
NIPS'15
"Regressive Virtual Metric Learning"
Michael Perrot and Amaury Habrard.
Proceedings of Advances in Neural Information Processing Systems (NIPS), 2015 (resources,pdf). -
ICML'15
"A Theoretical Analysis of Metric Hypothesis
Transfer Learning"
Michael Perrot and Amaury Habrard.
Proceedings of International Conference on Machine Learning (ICML), 2015 (resources, pdf). -
ECML'15
"Joint Semi-Supervised Similarity Learning for
Linear Classification"
M.-I. Nicolae, M. Sebban, A. Habrard, E. Gaussier
Proceedings of ECML/PKDD , 2015. -
ICONIP'15
"Algorithmic Robustness for Semi-Supervised (epsilon, gamma, tau)-Good Metric
Learning"
M.-I. Nicolae, M. Sebban, A. Habrard, E. Gaussier, M.-R. Amini.
Proceedings of ICONIP, 2015. -
BOOK "Metric Learning"
Aurélien Bellet, Amaury Habrard and Marc Sebban
Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, 2015. (link or link2, shorter arxiv version) -
Neurocomputing "Robustness and
Generalization for Metric Learning"
Aurélien Bellet, Amaury Habrard
Neurocomputing, vol 151, pages 259-267, 2015. (pdf, more resources) -
MLJ "Learning a priori constrained weighted majority votes"
Aurélien Bellet, Amaury Habrard, Emilie Morvant, Marc Sebban
Machine Learning, vol 97(1-2), p. 129-154, 2014. (pdf) - ECCV'14 "Modeling Perceptual Color
Differences by Local Metric Learning"
Michaël Perrot, Amaury Habrard, Damien Muselet, Marc Sebban
Proceedings of the European Conference on Computer Vision (ECCV), 2014. (pdf, data sets and resources, supplementary, DOI) - ICML'14 "Dimension-free Concentration Bounds on Hankel Matrices for Spectral
Learning"
Francois Denis, Mattias Gybels, Amaury Habrard
Proceedings of the International Conference on Machine Learning (ICML), 2014. (pdf) - SSPR'14 "
Majority Vote of Diverse Classifiers for Late Fusion"
Emilie Morvant, Amaury Habrard, Stéphane Ayache
Structural and Syntactic Pattern Recognition, Joint IAPR International Workshops (SSPR), 2014. (pdf) - ICGI'14 "Some
improvements of the spectral learning approach for probabilistic
grammatical inference"
Mattias Gybels, Francois Denis, Amaury Habrard
Proceeding of the International Conference on Grammatical Inference (ICGI), 2014. (pdf) - ICCV'13 "Unsupervised Visual Domain Adaptation Using Subspace Alignment"
Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars
Proceedings of the International Conference on Computer Vision (ICCV), 2013. (pdf, supplementary) - ICML'13 "A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers"
Pascal Germain, Amaury Habrard, Francois Laviolette, Emilie Morvant
Proceedings of the International Conference on Machine Learning (ICML), JMLR Workshop and conference proceedings, vol 28, no 3, p. 738-746, 2013. (online version, code) - ECML'13 "Boosting for Unsupervised Domain Adaptation"
Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban
Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), LNCS, 2013. (draft pdf) - IJAIT "Iterative Self-Labeling Domain Adaptation for Linear Structured Image Classification"
Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban
International Journal on Artificial Intelligence Tools, vol. 22, n. 5, 2013. (draft pdf) - KAIS "Parsimonious unsupervised and semi-supervised domain adaptation with good similarity functions."
Emilie Morvant, Amaury Habrard, Stéphane Ayache
Knowledge and Information Systems, vol 33, no 2, p.309-349, 2012 - MLJ "Good edit similarity learning by loss minimization."
Aurélien Bellet, Amaury Habrard, Marc Sebban
Machine Learning, vol.89, no 1-2, p. 5-35, 2012. (pdf, more resources) - ICML'12 "Similarity Learning for Provably Accurate Sparse Linear Classification."
Aurélien Bellet, Amaury Habrard, Marc Sebban
Proceedings of the International Conference on Machine Learning (ICML), 2012. (pdf, more resources) - ICGI'12 "Speeding Up Syntactic Learning Using Contextual Information."
Leonor Becerra-Bonache, Élisa Fromont, Amaury Habrard, Michaël Perrot, Marc Sebban
Proceedings of the International Conference on Grammatical Inference, JMLR Workshop and Conference Proceedings Track, vol 21, p. 49-53, (short paper), 2012. (pdf) - ICDM'11 "Sparse Domain Adaptation in Projection Spaces Based on Good Similarity Functions."
Emilie Morvant, Amaury Habrard, Stéphane Ayache:
Proceedings of the IEEE conference on International Conference on Data Mining, p. 457-466, 2011. (pdf) - ECML'11 "Learning Good Edit Similarities with Generalization Guarantees."
Aurélien Bellet, Amaury Habrard, Marc Sebban Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), part I, LNCS, p. 188-203, 2011. (pdf, more resources) - ICTAI'11 "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
Proceeding of the International Conference on Tools for Artificial Intelligence, p. 181-188, 2011. Best paper award. (pdf) - ICTAI'11 "An Experimental Study on Learning with Good Edit Similarity Functions."
Aurélien Bellet, Marc Sebban, Amaury Habrard
Proceeding of the International Conference on Tools for Artificial Intelligence, p. 181-188, 2011. (pdf) - SIMBAD'11 "On the Usefulness of Similarity Based Projection Spaces for Transfer Learning."
Emilie Morvant, Amaury Habrard, Stéphane Ayache
1st Interntational Workshop on Similarity-based Pattern Recognition, LNCS, 2011. (pdf) - JMLR "Using Contextual Representations to Efficiently Learn Context-Free Languages."
Alexander Clark, Rémi Eyraud and Amaury Habrard.
Journal of Machine Learning Research, vol 11, p2707-2744, 2010. (online version) -
ALT'10 "A Spectral Approach for Probabilistic Grammatical Inference on Trees"
Raphael Bailly, Amaury Habrard and Francois Denis.
21th International Conference on Algorithmic Learning Theory, LNCS 6331, p74-88, 2010. (pdf) -
ICTAI'09 "Learning Constrained Edit State Machines"
Laurent Boyer, Olivier Gandrillon, Amaury Habrard, Mathilde Pellerin and Marc Sebban.
21st International Conference on Tools with Artificiel Intelligence, p734-741, 2009.(pdf) -
CLAGI'09 "A note on contextual binary feature grammars"
Alexander Clark, Rémi Eyraud and Amaury Habrard.
ACL 2009 workshop on Computational Linguistic Aspects of Grammatical Inference, p33-40, 2009. (pdf) - PR "Learning probabilistic models of tree edit distance."
Laurent Boyer, Marc Bernard, Amaury Habrard and Marc Sebban.
Pattern Recognition, Vol 41, n8, p2611-2629, 2008. Elsevier Science. (pdf) -
SSPR'08 "Melody Recognition with Learned Edit Distances"
Amaury Habrard, Jose-Manuel Inesta, David Rizo, Marc Sebban
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR 2008 and SPR 2008, p86-96, Volume 5342 of LNCS, Springer. (draft pdf) -
ICGI'08 "A Polynomial Algorithm for the Inference of Context Free Languages"
Alexander Clark, Rémi Eyraud and Amaury Habrard.
9th International Colloquium on Grammatical Inference, p29-42, Volume 5278 of LNCS, Springer. pdf) -
ICGI'08 "Relevant Representations for the Inference of Rational Stochastic Tree Languages"
Francois Denis, Edouard Gilbert, Amaury Habrard, Faissal Ouardi and Marc Tommasi.
9th International Colloquium on Grammatical Inference, p57-70, Volume 5278 of LNCS, Springer. pdf -
ECML'08 "SEDiL: Software for Edit Distance Learning"
Laurent Boyer, Yann Esposito, Amaury Habrard, Jose Oncina and Marc Sebban.
19th European Conference on Machine Learning , p672-677, Volume 5212 of LNCS, Springer (draft pdf). -
ALT'07 "Learning rational stochastic tree languages"
Francois Denis and Amaury Habrard.
19th International Conference on Algorithmic Learning Theory, p242-256, 2007, Volume 4754 of LNCS, Springer..( draft .pdf ) -
ECML'07 "Learning Metrics between Tree Structured Data: Application to Image Recognition."
Laurent Boyer, Amaury Habrard and Marc Sebban.
18th European Conference on Machine Learning, p54-66, 2007, Volume 4701 of LNCS, Springer. ( draft .pdf ) -
ICGI'06 "Using Pseudo-stochastic Rational Languages in Probabilistic Grammatical Inference"
Amaury Habrard, Francois Denis and Yann Esposito.
8th International Colloquium on Grammatical Inference, p112-124, 2006, Volume 4201 of LNCS, Springer 2006. ( extended draft version with annex .pdf, report)
-
ICGI'06 "Learning multiplicity tree automata"
Amaury Habrard and Jose Oncina.
8th International Colloquium on Grammatical Inference. p268-280, 2006, Volume 4201 of LNCS, Springer. ( draft .pdf) -
ECML'06 "Learning Stochastic Tree Edit Distance"
Marc Bernard, Amaury Habrard and Marc Sebban.
17th European Conference on Machine Learning, p42-52, 2006, Volume 4212 of LNCS, Springer. ( draft .pdf ) -
COLT'06 "Learning rational stochastic languages"
Francois Denis, Yann Esposito and Amaury Habrard.
19th Annual Conference on Learning Theory, p274-288, 2006, Volume 4005 of LNCS, Springer.( draft .pdf ) - FUND INF "Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data."
Amaury Habrard, Marc Bernard and Marc Sebban.
Fundamenta Informaticae: Special Issue on Mining Graphs, Trees and Sequences, Vol 66, n1-2, p103-130, 2005. IOS Press. (draft .ps). -
FLAIRS'05 "Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms"
Amaury Habrard, Marc Bernard and Marc Sebban.
18th International Florida Artificial Intelligence Research Society Conference. p493-498, May 2005. AAAI Press.( draft .pdf) -
ECML'03 "Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference"
Amaury Habrard, Marc Bernard and Marc Sebban.
14th European Conference on Machine Learning. LNAI 2837, p169-180, 2003. ( .pdf) -
AIME'03 "Multi-Relational Data Mining of Medical Databases"
Amaury Habrard, Marc Bernard and Francois Jacquenet.
9th Conference on Artificial Intelligence for Medicine Europe, LNAI 2780, p365-374, 2003. ( pdf -
MGTS'03 "Probabilistic Approach for Reduction of Irrelevant Tree-structured Data"
Amaury Habrard, Marc Bernard and Marc Sebban.
1st International Workshop on Mining Graphs Trees and Sequences (co-located with ECML/PKDD-2003), p11-20, 2003. (.pdf) -
ICGI'02 "Generalized Stochastic Tree Automata for Multi-Relational Data Mining"
Amaury Habrard, Marc Bernard and Francois Jacquenet.
6th International Colloquium on Grammatical Inference. LNAI 2484, p120-133, 2002. ( pdf)
Other informations
- Publications tracked by DBLP
- By google scholar
Book on Metric Learning
Our book published by Morgan & Claypool
can be downloaded online
here
and is also available on here.
A
shorter arXiv version can be found here.
Research reports
- A Survey on Metric Learning for Feature Vectors and Structured Data.
Aurélien Bellet, Amaury Habrard, Marc Sebban
Technical report, 2013. pdf, more resources