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Marc Sebban
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Publications
- 2023
- [c73]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 - [c72]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 - 2022
- [j33]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) - [j32]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) - 2021
- [j31]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) - [j29]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) - 2020
- [j28]Léo Gautheron, Amaury Habrard, Emilie Morvant, Marc Sebban:
Metric Learning from Imbalanced Data with Generalization Guarantees. Pattern Recognit. Lett. 133: 298-304 (2020) - [c66]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 - [c64]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 - [i11]Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, Younès Bennani:
A survey on domain adaptation theory. CoRR abs/2004.11829 (2020) - 2019
- [j27]Ievgen Redko, Amaury Habrard, Marc Sebban:
On the analysis of adaptability in multi-source domain adaptation. Mach. Learn. 108(8-9): 1635-1652 (2019) - [j26]Tien-Nam Le, Amaury Habrard, Marc Sebban:
Deep multi-Wasserstein unsupervised domain adaptation. Pattern Recognit. Lett. 125: 249-255 (2019) - [c62]Kevin Bascol, Rémi Emonet, Élisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban:
From Cost-Sensitive to Tight F-measure Bounds. AISTATS 2019: 1245-1253 - [c61]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 - [c60]Léo Gautheron, Amaury Habrard, Emilie Morvant, Marc Sebban:
Metric Learning from Imbalanced Data. ICTAI 2019: 923-930 - [c59]Nam Lê Tien, Amaury Habrard, Marc Sebban:
Differentially Private Optimal Transport: Application to Domain Adaptation. IJCAI 2019: 2852-2858 - [i10]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) - [i9]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) - [i8]Léo Gautheron, Emilie Morvant, Amaury Habrard, Marc Sebban:
Metric Learning from Imbalanced Data. CoRR abs/1909.01651 (2019) - 2018
- [j25]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) - [c58]Jordan Fréry, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton:
Online Non-linear Gradient Boosting in Multi-latent Spaces. IDA 2018: 99-110 - [c57]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 - [c56]Jordan Fréry, Amaury Habrard, Marc Sebban, Liyun He-Guelton:
Non-Linear Gradient Boosting for Class-Imbalance Learning. LIDTA@ECML/PKDD 2018: 38-51 - 2017
- [c54]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 - [c53]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 - 2016
- [j23]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) - [j22]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) - [i5]Ievgen Redko, Amaury Habrard, Marc Sebban:
Theoretical Analysis of Domain Adaptation with Optimal Transport. CoRR abs/1610.04420 (2016) - [i4]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 - [c48]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 - [c47]Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban:
Joint Semi-supervised Similarity Learning for Linear Classification. ECML/PKDD (1) 2015: 594-609 - [c45]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 - 2014
- [j21]Aurélien Bellet, Amaury Habrard, Emilie Morvant, Marc Sebban:
Learning a priori constrained weighted majority votes. Mach. Learn. 97(1-2): 129-154 (2014) - [c44]Michaël Perrot, Amaury Habrard, Damien Muselet, Marc Sebban:
Modeling Perceptual Color Differences by Local Metric Learning. ECCV (5) 2014: 96-111 - [i3]Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars:
Subspace Alignment For Domain Adaptation. CoRR abs/1409.5241 (2014) - 2013
- [j20]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) - [c43]Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars:
Unsupervised Visual Domain Adaptation Using Subspace Alignment. ICCV 2013: 2960-2967 - [c42]Amaury Habrard, Jean-Philippe Peyrache, Marc Sebban:
Boosting for Unsupervised Domain Adaptation. ECML/PKDD (2) 2013: 433-448 - [i1]Aurélien Bellet, Amaury Habrard, Marc Sebban:
A Survey on Metric Learning for Feature Vectors and Structured Data. CoRR abs/1306.6709 (2013) - 2012
- [j19]Aurélien Bellet, Amaury Habrard, Marc Sebban:
Good edit similarity learning by loss minimization. Mach. Learn. 89(1-2): 5-35 (2012) - [c40]Aurélien Bellet, Amaury Habrard, Marc Sebban:
Similarity Learning for Provably Accurate Sparse Linear Classification. ICML 2012 - [c39]Leonor Becerra-Bonache, Élisa Fromont, Amaury Habrard, Michaël Perrot, Marc Sebban:
Speeding Up Syntactic Learning Using Contextual Information. ICGI 2012: 49-53 - 2011
- [c38]Aurélien Bellet, Marc Sebban, Amaury Habrard:
An Experimental Study on Learning with Good Edit Similarity Functions. ICTAI 2011: 126-133 - [c37]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 - [c35]Aurélien Bellet, Amaury Habrard, Marc Sebban:
Learning Good Edit Similarities with Generalization Guarantees. ECML/PKDD (1) 2011: 188-203 - 2009
- [c33]Laurent Boyer, Olivier Gandrillon, Amaury Habrard, Mathilde Pellerin, Marc Sebban:
Learning Constrained Edit State Machines. ICTAI 2009: 734-741 - 2008
- [j13]Marc Bernard, Laurent Boyer, Amaury Habrard, Marc Sebban:
Learning probabilistic models of tree edit distance. Pattern Recognit. 41(8): 2611-2629 (2008) - [c31]Laurent Boyer, Yann Esposito, Amaury Habrard, José Oncina, Marc Sebban:
SEDiL: Software for Edit Distance Learning. ECML/PKDD (2) 2008: 672-677 - [c30]Amaury Habrard, José Manuel Iñesta Quereda, David Rizo, Marc Sebban:
Melody Recognition with Learned Edit Distances. SSPR/SPR 2008: 86-96 - 2007
- [c29]Laurent Boyer, Amaury Habrard, Marc Sebban:
Learning Metrics Between Tree Structured Data: Application to Image Recognition. ECML 2007: 54-66 - 2006
- [c27]Marc Bernard, Amaury Habrard, Marc Sebban:
Learning Stochastic Tree Edit Distance. ECML 2006: 42-53 - 2005
- [j11]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) - [c22]Amaury Habrard, Marc Bernard, Marc Sebban:
Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms. FLAIRS 2005: 493-498 - 2003
- [c19]Amaury Habrard, Marc Bernard, Marc Sebban:
Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference. ECML 2003: 169-180
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