| 2025 | ICML | A Bregman Proximal Viewpoint on Neural Operators. | Abdel-Rahim Mezidi, Jordan Patracone, Saverio Salzo, Amaury Habrard, Massimiliano Pontil, Rmi Emonet, Marc Sebban |
| 2025 | ICTAI | Physics-Informed Machine Learning for Modeling CO2 Capture from Scarce Data. | Mickael Gault, Pierre Bachaud, Benot Celse, Rmi Emonet, Marc Sebban |
| 2024 | IJCNN | Generative shape deformation with optimal transport using learned transformations. | Jorge Azorn Lpez, Marc Sebban, Nahuel E. Garcia-D'Urso, Amaury Habrard, Andrs Fuster Guill |
| 2024 | ICTAI | Physics-Informed Machine Learning for Better Understanding Laser-Matter Interaction. | Fayad Ali Banna, Jean-Philippe Colombier, Rmi Emonet, Marc Sebban |
| 2024 | ICTAI | Unsupervised Learning and Effective Complexity: Introducing JPG and Neural Sophistication. | Erick Gomez Soto, Rmi Emonet, Marc Sebban |
| 2022 | AAAI | Optimal Tensor Transport. | Tanguy Kerdoncuff, Rmi Emonet, Michal Perrot, Marc Sebban |
| 2022 | ICASSP | Fast Multiscale Diffusion On Graphs. | Sibylle Marcotte, Amlie Barbe, Rmi Gribonval, Titouan Vayer, Marc Sebban, Pierre Borgnat, Paulo Gonalves |
| 2021 | ICTAI | Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain Adaptation. | Amlie Barbe, Paulo Gonalves, Marc Sebban, Pierre Borgnat, Rmi Gribonval, Titouan Vayer |
| 2020 | ICML | A Swiss Army Knife for Minimax Optimal Transport. | Sofien Dhouib, Ievgen Redko, Tanguy Kerdoncuff, Rmi Emonet, Marc Sebban |
| 2020 | IJCAI | Metric Learning in Optimal Transport for Domain Adaptation. | Tanguy Kerdoncuff, Rmi Emonet, Marc Sebban |
| 2020 | IJCAI | Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data. | Rmi Viola, Rmi Emonet, Amaury Habrard, Guillaume Metzler, Marc Sebban |
| 2019 | AISTATS | From Cost-Sensitive to Tight F-measure Bounds. | Kevin Bascol, Rmi Emonet, lisa Fromont, Amaury Habrard, Guillaume Metzler, Marc Sebban |
| 2019 | IJCAI | Differentially Private Optimal Transport: Application to Domain Adaptation. | Nam L Tien, Amaury Habrard, Marc Sebban |
| 2019 | ICTAI | Metric Learning from Imbalanced Data. | Lo Gautheron, Amaury Habrard, Emilie Morvant, Marc Sebban |
| 2019 | ICTAI | An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data. | Rmi Viola, Rmi Emonet, Amaury Habrard, Guillaume Metzler, Sbastien Riou, Marc Sebban |
| 2018 | IDA | Online Non-linear Gradient Boosting in Multi-latent Spaces. | Jordan Frry, Amaury Habrard, Marc Sebban, Olivier Caelen, Liyun He-Guelton |
| 2018 | IDA | Tree-Based Cost Sensitive Methods for Fraud Detection in Imbalanced Data. | Guillaume Metzler, Xavier Badiche, Brahim Belkasmi, lisa Fromont, Amaury Habrard, Marc Sebban |
| 2016 | CVPR | Metric Learning as Convex Combinations of Local Models with Generalization Guarantees. | Valentina Zantedeschi, Rmi Emonet, Marc Sebban |
| 2015 | CVPR | Landmarks-based kernelized subspace alignment for unsupervised domain adaptation. | Rahaf Aljundi, Rmi Emonet, Damien Muselet, Marc Sebban |
| 2015 | ICONIP | Algorithmic Robustness for Semi-Supervised (ε, γ, τ) -Good Metric Learning. | Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, ric Gaussier, Massih-Reza Amini |
| 2014 | ECCV | Modeling Perceptual Color Differences by Local Metric Learning. | Michal Perrot, Amaury Habrard, Damien Muselet, Marc Sebban |
| 2013 | ICCV | Unsupervised Visual Domain Adaptation Using Subspace Alignment. | Basura Fernando, Amaury Habrard, Marc Sebban, Tinne Tuytelaars |
| 2012 | CVPR | Discriminative feature fusion for image classification. | Basura Fernando, lisa Fromont, Damien Muselet, Marc Sebban |
| 2012 | ICML | Similarity Learning for Provably Accurate Sparse Linear Classification. | Aurlien Bellet, Amaury Habrard, Marc Sebban |
| 2011 | ICTAI | An Experimental Study on Learning with Good Edit Similarity Functions. | Aurlien Bellet, Marc Sebban, Amaury Habrard |
| 2011 | ICTAI | Using the H-Divergence to Prune Probabilistic Automata. | Marc Bernard, Baptiste Jeudy, Jean-Philippe Peyrache, Marc Sebban, Franck Thollard |
| 2011 | ICTAI | 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 |
| 2009 | ICTAI | Learning Constrained Edit State Machines. | Laurent Boyer, Olivier Gandrillon, Amaury Habrard, Mathilde Pellerin, Marc Sebban |
| 2008 | SSPR | Melody Recognition with Learned Edit Distances. | Amaury Habrard, Jos Manuel Iesta Quereda, David Rizo, Marc Sebban |
| 2006 | ICTAI | Sequence Mining Without Sequences: A New Way for Privacy Preserving. | Stphanie Jacquemont, Franois Jacquenet, Marc Sebban |
| 2006 | SSPR | Using Learned Conditional Distributions as Edit Distance. | Jos Oncina, Marc Sebban |
| 2005 | FlAIRS | Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms. | Amaury Habrard, Marc Bernard, Marc Sebban |
| 2004 | ICML | Boosting grammatical inference with confidence oracles. | Jean-Christophe Janodet, Richard Nock, Marc Sebban, Henri-Maxime Suchier |
| 2004 | ICTAI | Mining Decision Rules from Deterministic Finite Automata. | Franois Jacquenet, Marc Sebban, Georges Valtudie |
| 2003 | ICML | On State Merging in Grammatical Inference: A Statistical Approach for Dealing with Noisy Data. | Marc Sebban, Jean-Christophe Janodet |
| 2001 | FlAIRS | Improvement of Nearest-Neighbor Classifiers via Support Vector Machines. | Marc Sebban, Richard Nock |
| 2001 | ICML | Boosting Neighborhood-Based Classifiers. | Marc Sebban, Richard Nock, Stphane Lallich |
| 2000 | AI | Identifying and Eliminating Irrelevant Instances Using Information Theory. | Marc Sebban, Richard Nock |
| 2000 | ALT | Sharper Bounds for the Hardness of Prototype and Feature Selection. | Richard Nock, Marc Sebban |
| 2000 | FlAIRS | A Boosting-Based Prototype Weighting and Selection Scheme. | Richard Nock, Marc Sebban |
| 2000 | ICML | Instance Pruning as an Information Preserving Problem. | Marc Sebban, Richard Nock |
| 2000 | UAI | Combining Feature and Example Pruning by Uncertainty Minimization. | Marc Sebban, Richard Nock |
| 1999 | IDA | From Theoretical Learnability to Statistical Measures of the Learnable. | Marc Sebban, Gilles Richard |
| 1998 | AI | Strings Clustering and Statistical Validation of Clusters. | Marc Sebban, Anne M. Landraud |
| 1998 | FlAIRS | Prototype Selection from Homogeneous Subsets by a Monte Carlo Sampling. | Marc Sebban |