Marc Sebban
Publications
Publications
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Learning dynamics in ultrafast laser self-organizationProgress in Ultrafast Laser Modifications of Materials, Jun 2025, Villars-sur-Ollon, France |
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Physics-Informed Machine Learning for Modeling CO2 Capture from Scarce Data37th IEEE International Conference on Tools with Artificial Intelligence, Institute of Electrical and Electronics Engineers, Nov 2025, Athènes, Greece. pp.1436-1442, ⟨10.1109/ICTAI66417.2025.00207⟩ |
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Provably Accurate Adaptive Sampling for Collocation Points in Physics-informed Neural NetworksEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2025, Porto, Portugal. pp.19-37, ⟨10.1007/978-3-032-06096-9_2⟩ |
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Méthode de quadrature pour les PINNs fondée théoriquement sur la hessienne des résiduels30ème Colloque sur le traitement du signal et des images GRETSI, Aug 2025, Strasbourg, France |
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A Bregman Proximal Viewpoint on Neural OperatorsInternational Conference on Machine Learning, Jul 2025, Vancouver, Canada |
Generative shape deformation with optimal transport using learned transformationsInternational Joint Conference on Neural Networks (IJCNN), Jun 2024, YOKOHAMA, Japan |
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Physics-informed Machine Learning for Better Understanding Laser-Matter InteractionThe 36th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2024), IEEE, Oct 2024, Herndon, VA, United States. pp.7 |
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Deciphering the complexity behind laser-induced selforganized nanopatterns17th International Conference on Laser Ablation (COLA 2024), Sep 2024, Hersonissos, Greece |
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Approximation Error of Sobolev Regular Functions with tanh Neural Networks: Theoretical Impact on PINNsECML PKDD 2024 - Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Sep 2024, Vilnius, Lithuania |
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Unsupervised Learning and Effective Complexity: introducing JPG and Neural SophisticationInternational Conference on Tools with Artificial Intelligence (ICTAI), Oct 2024, Herndon, United States |
Overview of total ionizing dose levels in the Large Hadron Collider during 2022 restart14th International Particle Accelerator Conference, May 2023, Geneva, Switzerland. ⟨10.18429/jacow-ipac2023-thpa047⟩ |
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Is My Neural Net Driven by the MDL Principle?European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2023, Turin, France. pp.173-189, ⟨10.1007/978-3-031-43415-0_11⟩ |
optimized laser induced colors and image multiplexing on plasmonic quasi-random metasurfaces using deep learning12th international conference on metamaterials, Jul 2022, torremolinos, Spain |
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Why do State-of-the-art Super-Resolution Methods not work well for Bone Microstructure CT Imaging?EUSIPCO 2022, Aug 2022, Belgrade, France. ⟨10.23919/EUSIPCO55093.2022.9909945⟩ |
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Optimal Tensor TransportAAAI, Feb 2022, Vancouver, Canada. ⟨10.1609/aaai.v36i7.20672⟩ |
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Stochasticity versus determinism in LIPSS formation10th International Workshop LIPSS, Sep 2022, Orléans, France |
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Fast Multiscale Diffusion on GraphsICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapore, Singapore. ⟨10.1109/ICASSP43922.2022.9746802⟩ |
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Optimization of the Diffusion Time in Graph Diffused-Wasserstein Distances: Application to Domain AdaptationICTAI 2021 - 33rd IEEE International Conference on Tools with Artificial Intelligence, Nov 2021, Virtual conference, France. pp.1-8, ⟨10.1109/ICTAI52525.2021.00125⟩ |
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Landmark-based Ensemble Learning with Random Fourier Features and Gradient BoostingEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent, Belgium. ⟨10.1007/978-3-030-67664-3_9⟩ |
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A Swiss Army Knife for Minimax Optimal TransportThirty-seventh International Conference on Machine Learning, Jul 2020, Vienne, Austria |
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Metric Learning in Optimal Transport for Domain AdaptationInternational Joint Conference on Artificial Intelligence, Jan 2020, Kyoto, Japan. pp.2162-2168, ⟨10.24963/ijcai.2020/299⟩ |
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Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to deal with Imbalanced DataIJCAI 2020, the 29th International Joint Conference on Artificial Intelligence, Jul 2020, Yokohama, Japan. pp.2155-2161, ⟨10.24963/ijcai.2020/298⟩ |
MLFP: Un algorithme d'apprentissage de métrique pour la classification de données déséquilibréesConférence sur l'Apprentissage automatique (CAp 2020), Jun 2020, Vannes, France |
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Graph Diffusion Wasserstein DistancesECML PKDD 2020 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent, Belgium. pp.1-16, ⟨10.1007/978-3-030-67661-2_34⟩ |
Transport Optimal entre Graphes exploitant la Diffusion de la ChaleurCAP 2020 - Conférence sur l'Apprentissage Automatique, Nov 2020, Vannes, France |
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Metric Learning from Imbalanced DataInternational Conference on Tools with Artificial Intelligence (ICTAI), Nov 2019, Portland, Oregon, United States. ⟨10.1109/ictai.2019.00131⟩ |
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Fast and Provably Effective Multi-view Classification with Landmark-based SVMECML PKDD 2018, Sep 2018, Dublin, Ireland. pp.193-208, ⟨10.1007/978-3-030-10928-8_12⟩ |
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Transport Optimal sous Contrainte de Régularité pour l'Adaptation de Domaines entre Graphes avec AttributsGRETSI 2019 - XXVIIème Colloque francophonede traitement du signal et des images, Aug 2019, Lille, France. pp.1-4 |
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An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced DataInternational Conference on Tools with Artificial Intelligence (ICTAI), Nov 2019, Portland, Oregon, United States. ⟨10.1109/ICTAI.2019.00042⟩ |
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From Cost-Sensitive Classification to Tight F-measure BoundsAISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, Apr 2019, Naha, Okinawa, Japan. pp.1245-1253 |
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Une version corrigée de l’algorithme des plus proches voisins pour l’optimisation de la F-mesure dans un contexte déséquilibréConférence sur l'Apprentissage automatique (CAp 2019), Jul 2019, Toulouse, France |
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Differentially Private Optimal Transport: Application to Domain AdaptationIJCAI 2019, Aug 2019, Macao, China. pp.2852-2858, ⟨10.24963/ijcai.2019/395⟩ |
Revisite des "random Fourier features" basée sur l'apprentissage PAC-Bayésien via des points d'intérêtsCAp 2019 - Conférence sur l'Apprentissage automatique, Jul 2019, Toulouse, France |
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Un algorithme de pondération de la F-Mesure par pondération des erreurs de classfication.Conférence pour l'Apprentissage Automatique, Jun 2018, Saint-Etienne du Rouvray, France |
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Tree-based Cost-Sensitive Methods for Fraud Detection in Imbalanced DataIDA 2018 - 17th International Symposium on Intelligent Data Analysis, Oct 2018, ‘s-Hertogenbosch, Netherlands. pp.213-224, ⟨10.1007/978-3-030-01768-2_18⟩ |
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Online Non-Linear Gradient Boosting in Multi-Latent Spaces17th International Symposium on Intelligent Data Analysis (IDA'18), Oct 2018, s-Hertogenbosch, Netherlands |
Apprentissage de métrique pour la classification supervisée de données déséquilibréesConférence sur l'Apprentissage Automatique, Jun 2018, Rouen, France |
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Efficient top rank optimization with gradient boosting for supervised anomaly detectionEuropean Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD'17), Sep 2017, Skopje, Macedonia |
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Apprentissage de sphères maximales d’exclusion avec garanties théoriquesConférence sur l'Apprentissage Automatique, Jun 2017, Grenoble, France |
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Theoretical Analysis of Domain Adaptation with Optimal TransportECML PKDD 2017, Sep 2017, Skopje, Macedonia |
Apprentissage de Combinaisons Convexes de Métriques Locales avec Garanties de GénéralisationCAp2016, Jul 2016, Marseille, France |
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beta-risk: a New Surrogate Risk for Learning from Weakly Labeled DataNIPS 2016, Dec 2016, Barcelona, Spain |
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Metric Learning as Convex Combinations of Local Models with Generalization GuaranteesCVPR2016, Jun 2016, Las Vegas, United States |
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Computing Image Descriptors from Annotations Acquired from External ToolsROBOT 2015: Second Iberian Robotics Conference, Nov 2015, Lisbon, Portugal |
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Supervised Spectral Subspace Clustering for Visual Dictionary Creation in the Context of Image ClassificationACPR 2015: 3rd IAPR Asian Conference on Pattern Recognition, Nov 2015, Kuala Lumpur, Malaysia |
Algorithmic Robustness for Semi-Supervised (ε, γ, τ )-Good Metric LearningInternational Conference on Neural Information Processing ICONIP, Nov 2015, Istanbul, Turkey. pp.10 |
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Landmarks-based Kernelized Subspace Alignment for Unsupervised Domain AdaptationComputer Vision and Pattern Recognition (CVPR'2015), Jun 2015, Boston, United States |
Joint Semi-supervised Similarity Learning for Linear ClassificationECML-PKDD 2015, Sep 2015, Porto, Portugal. ⟨10.1007/978-3-319-23528-837⟩ |
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Modeling Perceptual Color Differences by Local Metric LearningEuropean Conference on Computer Vision, Sep 2014, Zurich, Switzerland |
Modélisation de Distances Couleur Uniformes par Apprentissage de Métriques LocalesCAp'2014 : Conférence d'Apprentissage Automatique, Jul 2014, Saint-Étienne, France |
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Vote de majorité a priori contraint pour la classification binaire : spécification au cas des plus proches voisinsConférence sur l'Apprentissage Automatique (CAp), Jul 2013, Lille, France |
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Unsupervised Visual Domain Adaptation Using Subspace AlignmentICCV 2013, Dec 2013, Sydney, Australia. pp.2960-2967 |
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Boosting for Unsupervised Domain AdaptationECML PKDD 2013, Sep 2013, Prague, Czech Republic. pp.433-448 |
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Apprentissage de bonnes similarités pour la classification linéaire parcimonieuseConférence Francophone sur l'Apprentissage Automatique - CAp 2012, May 2012, Nancy, France. 16 p |
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Handwritten Digit Recognition using Edit Distance-Based KNNTeaching Machine Learning Workshop, Jun 2012, Edinburgh, Scotland, United Kingdom |
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Speeding Up Syntactic Learning Using Contextual InformationInternational Conference on Grammatical Inference, Sep 2012, United States. pp.49-53 |
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Discriminative Feature Fusion for Image ClassificationComputer Vision and Pattern Recognition, Jun 2012, Rhode Island, United States. pp.3434-3441 |
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Un Cadre Formel de Boosting pour l'Adaptation de DomaineConférence Francophone sur l'Apprentissage Automatique - CAp 2012, Laurent Bougrain, May 2012, Nancy, France. 16 p |
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Similarity Learning for Provably Accurate Sparse Linear ClassificationInternational Conference on Machine Learning, Jun 2012, United Kingdom |
Using the H-divergence to Prune Probabilistic AutomataICTAI 2011, Nov 2011, Boca Raton, United States |
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Learning Good Edit Similarities with Generalization GuaranteesEuropean Conference on Machine Learning, Sep 2011, Athens, Greece. pp.188-203 |
Domain Adaptation with Good Edit Similarities: a Sparse Way to deal with Scaling and Rotation Problems in Image ClassificationICTAI 2011, Nov 2011, United States |
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An Experimental Study on Learning with Good Edit Similarity FunctionsICTAI 2011, Nov 2011, United States |
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Weighted Symbols-based Edit Distance for String-Structured Image ClassificationECML PKDD 2010 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases., Sep 2010, Barcelona, Spain |
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Accurate Visual Word Construction using a Supervised Approach25th International Conference of Image and Vision Computing New Zealand, Nov 2010, New Zealand |
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Discovering Patterns in Flows: a Privacy Preserving Approach with the ACSM PrototypeECML PKDD, Sep 2009, Bled, Slovenia. pp.734--737 |
Apprentissage de noyaux d'édition de séquencesConférence d'Apprentissage : CAP 2009, May 2009, Hammamet, Tunisia. pp.à venir |
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Learning Constrained Edit State Machines21st IEEE International Conference on Tools with Artificial Intelligence, Nov 2009, United States. pp.734-741 |
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SEDiL: Software for Edit Distance LearningEuropean Conference on Machine Learning (ECML 2008), Sep 2008, Belgium. pp.672-677 |
Learning String Edit Similarities using Constrained Finite State MachinesCAp'08, May 2008, Porquerolles, France. pp.37-52 |
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Melody Recognition with Learned Edit DistancesStructural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR 2008 and SPR 2008, Dec 2008, Orlando, United States. pp.86-96 |
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Correct your Text with GoogleIEEE International Conference on Web Intelligence, Nov 2007, Fremont, United States. pp.xx-xx |
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Learning Metrics between Tree Structured Data: Application to Image Recognition18th European Conference on Machine Learning (ECML), Sep 2007, Warsaw, Poland. pp.54-66 |
Learning Conditional Transducers for Estimating the Distribution of String Edit CostsGrammatical Inference: workshop on open problems and new directions, May 2006, Saint-Etienne, France |
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Sequence Mining Without Sequences: a New Way for Privacy Preserving2006, pp.347-354 |
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Using Learned Conditional Distributions as Edit DistanceStructural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Aug 2006, Hong Kong, China. pp 403-411, ISBN 3-540-37236-9 |
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A Discriminative Model of Stochastic Edit Distance in the form of a Conditional Transducer8th International Colloquium on Grammatical Inference, Sep 2006, Tokyo, Japan. pp.240-252, ⟨10.1007/11872436_20⟩ |
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Learning Stochastic Tree Edit Distance17th European Conference on Machine Learning, Sep 2006, Berlin, Germany. pp.42-53 |
Boosting d'un pool d'apprenants faiblesCAp 2006, May 2006, Trégastel, France. pp.283-298 |
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Constrained Sequence Mining based on Probabilistic Finite State AutomataConférence d'Apprentissage Automatique, 2005, Nice, France. pp.15-30 |
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Correction of Uniformly Noisy Distributions to Improve Probabilistic Grammatical Inference Algorithms18th International Florida Artificial Intelligence Research Society conference, May 2005, Miramar Beach, Florida, United States. pp.493-498 |
Constrained Sequence Mining based on Probabilistic Finite State AutomataWorkshop on Mining Graphs, Trees and Structured Data at ECML/PKDD, Oct 2005, Porto, Portugal |
Predicting laser energy absorption on nanostructured surfaces with deep learningMachine Learning in Photonics, Apr 2024, Strasbourg, France. SPIE, pp.74, ⟨10.1117/12.3022317⟩ |
Advances in Domain Adaptation TheoryElsevier; ISTE, 2019, 9781785482366 |
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Metric LearningMorgan & Claypool Publishers (USA), Synthesis Lectures on Artificial Intelligence and Machine Learning, pp 1-151, 9 (1), pp.1-151, 2015, Synthesis Lectures on Artificial Intelligence and Machine Learning, ⟨10.2200/S00626ED1V01Y201501AIM030⟩ |
Proceddings of 16ème Conférence d’Apprentissage CAp’20142014 |
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Unsupervised Domain Adaptation Based on Subspace AlignmentDomain Adaptation in Computer Vision Applications. Advances in Computer Vision and Pattern Recognition, Springer International Publishing, pp.81-94, 2017, Advances in Computer Vision and Pattern Recognition, ⟨10.1007/978-3-319-58347-1_4⟩ |
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Photonic Self-Learning in Ultrafast Laser-Induced Complexity2025 |
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Unrolled-SINDy: A Stable Explicit Method for Non linear PDE Discovery from Sparsely Sampled Data2025 |
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Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting2019 |
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A survey on domain adaptation theory: learning bounds and theoretical guaranteesArXiv:2004.11829. 2020 |
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A Survey on Metric Learning for Feature Vectors and Structured Data[Research Report] Laboratoire Hubert Curien UMR 5516. 2013 |