Amaury Habrard
Publications
Publications
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Douglas-Rachford Splitting for Hybrid Differentiable ModelsWorkshop on Differentiable Systems and Scientific Machine Learning (DiffSys), EurIPS, Dec 2025, Copenhagen, Denmark |
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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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Contextual Hypernetwork for Adaptive Prediction of Laser-Induced Colors on Quasi-Random Plasmonic MetasurfacesEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2025, Porto (Portugal), France |
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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 |
Decoding Ornaments: The Case of Marc-Michel Rey’s DatabaseWorkshop on Methods for Micro- and Mesotypography, Johannes Gutenberg-Universität Mainz, Sep 2025, Wolfenbüttel, Germany |
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Historical benchmark Dataset for multiple computer vision tasks: Discuss limitations of different Machine Learning techniquesIntermediality and Computational Humanities Hackathon, University of Vienna, Nov 2024, Vienna, Austria |
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Advanced Traffic Engineering in WAN Using Graph Attention NetworksThe 20th International Conference on Wireless and Mobile Computing, Networking and Communications, Wimob 2024, Oct 2024, Paris, France |
Generative shape deformation with optimal transport using learned transformationsInternational Joint Conference on Neural Networks (IJCNN), Jun 2024, YOKOHAMA, Japan |
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A PAC-Bayes Analysis of Adversarial RobustnessThirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), NIPS: Neural Information Processing Systems Foundation, Dec 2021, Virtual-only Conference, Australia. 1105, pp. 14421 - 14433, ⟨10.5555/3540261.3541366⟩ |
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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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Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity MeasuresAISTATS 2024 - 27th International Conference on Artificial Intelligence and Statistics, May 2024, Valencia, Spain |
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A Theoretically Grounded Extension of Universal Attacks from the Attacker's ViewpointECML PKDD 2024 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2024, Vilnius, Lithuania. pp.1-27, ⟨10.1007/978-3-031-70359-1_17⟩ |
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Length Independent PAC-Bayes Bounds for Simple RNNsAISTATS 2024 - 27th International Conference on Artificial Intelligence and Statistics, May 2024, Valence, Spain |
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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 |
Vignettes discovery in historical ornaments: Rey database and practices, limits and advancesWorkshop "Computing the Page in Early-Modern Europe", Visual Geometry Group, Mar 2024, Oxford, United Kingdom |
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Towards few-annotation learning for object detection: are transformer-based models more efficient ?WACV2023 - 2023 IEEE-CVF Winter Conference on Applications of Computer Vision, Jan 2023, Waikoloa, HI, United States. pp.75-84, ⟨10.1109/WACV56688.2023.00016⟩ |
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Proposal-contrastive pretraining for object detection from fewer dataICLR 2023 - The Eleventh International Conference on Learning Representations, May 2023, Kigali, Rwanda. https://openreview.net/forum?id=gm0VZ-h-hPy |
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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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A Simple Way to Learn Metrics Between Attributed GraphsProceedings of the First Learning on Graphs Conference (LoG 2022),, Dec 2022, Virtual Event (Republic of Korea), France |
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization BoundCAp 2022, Jul 2022, Vannes, France |
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Stochasticity versus determinism in LIPSS formation10th International Workshop LIPSS, Sep 2022, Orléans, France |
Intérêt des bornes désintégrées pour la généralisation avec des mesures de complexitéCAp 2022, Jul 2022, Vannes, France |
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Improving few-shot learning through multi-task representation learning theory17th European Conference on Computer Vision – ECCV 2022, Oct 2022, Tel Aviv, Israel. pp.435-452, ⟨10.1007/978-3-031-20044-1_25⟩ |
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Self-Bounding Majority Vote Learning Algorithms by the Direct Minimization of a Tight PAC-Bayesian C-BoundECML PKDD 2021, Sep 2021, Bilbao, Spain. ⟨10.1007/978-3-030-86520-7_11⟩ |
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Multiview Variational Graph Autoencoders for Canonical Correlation AnalysisICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun 2021, Toronto, Canada. pp.5320-5324, ⟨10.1109/ICASSP39728.2021.9414466⟩ |
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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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Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization BoundNeurIPS, 2021, Online, France. ⟨10.5555/3540261.3540296⟩ |
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Online Performance Evaluation of Deep Learning Networks for Profiled Side-Channel AnalysisInternational Workshop on Constructive Side-Channel Analysis and Secure Design (COSADE), Apr 2020, Lugano ( virtual ), Switzerland. pp.200-218, ⟨10.1007/978-3-030-68773-1_10⟩ |
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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⟩ |
Anomaly Detection, Consider your Dataset First, An illustration on Fraud Detection31st International Conference on Tools with Artificial Intelligence (ICTAI), Nov 2019, Portland, United States. ⟨10.1109/ICTAI.2019.00188⟩ |
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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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Apprentissage profond pour les attaques par analyse de canaux auxiliaires des implémentations de fonctions cryptographiquesEcole d’hiver Francophone sur les Technologies de Conception des Systèmes embarqués Hétérogènes, FETCH, Feb 2020, Montréal, Canada |
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Putting theory to work: from learning bounds to meta-learning algorithmsWorkshop Meta-Learn@NeurIPS 2020, Dec 2020, Vancouver (Virtual conference), Canada |
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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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Dual Sequential Variational Autoencoders for Fraud Detection18th International Symposium on Intelligent Data Analysis (IDA), 2020, Konstanz, Germany. pp.14-26, ⟨10.1007/978-3-030-44584-3_2⟩ |
Improved Deep-Learning Side-Channel Attacks using Normalization layersWorkshop on Randomness and Arithmetics for Cryptography on Hardware (WRAC’H), Apr 2019, Roscoff, France |
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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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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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Near-lossless Binarization of Word Embeddings33rd AAAI Conference on Artificial Intelligence (AAAI-19), Jan 2019, Honolulu, HI, United States |
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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 |
Interpreting Neural Networks as Majority Votes through the PAC-Bayesian TheoryWorkshop on Machine Learning with guarantees @ NeurIPS 2019, Dec 2019, Vancouver, Canada |
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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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Dict2vec : Learning Word Embeddings using Lexical DictionariesConference on Empirical Methods in Natural Language Processing (EMNLP 2017), Sep 2017, Copenhague, Denmark. pp.254-263 |
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Theoretical Analysis of Domain Adaptation with Optimal TransportECML PKDD 2017, Sep 2017, Skopje, Macedonia |
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Joint distribution optimal transportation for domain adaptationNIPS 2017, Dec 2017, Los Angeles, United States |
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A New PAC-Bayesian Perspective on Domain Adaptation33rd International Conference on Machine Learning (ICML 2016), Jun 2016, New York, NY, United States |
Bornes en Généralisation à Convergence Rapide pour le Transfert d'Hypothèses en Apprentissage de MétriquesConférence francophone sur l'Apprentissage Automatique, Jul 2016, Marseille, France |
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Mapping Estimation for Discrete Optimal TransportNeural Information Processing System, Dec 2016, Barcelone, Spain |
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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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A New PAC-Bayesian View of Domain AdaptationNIPS 2015 Workshop on Transfer and Multi-Task Learning: Trends and New Perspectives, Dec 2015, Montréal, Canada |
A Theoretical Analysis of Metric Hypothesis Transfer LearningInternational Conference on Machine Learning, Jul 2015, Lille, France |
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Regressive Virtual Metric LearningNeural Information Processing Systems, Dec 2015, Montréal, Canada |
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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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An Improvement to the Domain Adaptation Bound in a PAC-Bayesian contextNIPS 2014 Workshop on Transfer and Multi-task learning: Theory Meets Practice, Dec 2014, Montréal, Canada |
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Majority Vote of Diverse Classifiers for Late FusionIAPR Joint International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recignition, Aug 2014, Joensuu, Finland. pp.20 |
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Dimension-free Concentration Bounds on Hankel Matrices for Spectral LearningThe International Conference on Machine Learning (ICML), Jun 2014, China. pp.JMLR: W&CP volume 32 |
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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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Some improvements of the spectral learning approach for probabilistic grammatical inferenceProceedings of the 12th International Conference on Grammatical Inference (ICGI), Sep 2014, Kyoto, Japan. pp.64-78 |
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Une analyse PAC-Bayésienne de l'adaptation de domaine et sa spécialisation aux classifieurs linéairesConférence sur l'apprentissage automatique, Jul 2013, Villeneuve d'Ascq, France. pp.3 |
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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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A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear ClassifiersInternational Conference on Machine Learning 2013, Jun 2013, Atlanta, United States. pp.738-746 |
Utilisation de matrices de Hankel non bornées pour l'apprentissage spectral de langages stochastiquesConférence d'Apprentissage, 2013, France |
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Boosting for Unsupervised Domain AdaptationECML PKDD 2013, Sep 2013, Prague, Czech Republic. pp.433-448 |
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PAC-Bayesian Learning and Domain AdaptationMulti-Trade-offs in Machine Learning, NIPS 2012 Workshop, Dec 2012, Lake Tahoe, United States |
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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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Étude de la généralisation de DASF à l'adaptation de domaine semi-superviséeConférence Francophone sur l'Apprentissage Automatique - CAp 2012, May 2012, Nancy, France. pp.111-126 |
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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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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 |
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Sparse Domain Adaptation in Projection Spaces based on Good Similarity FunctionsIEEE International Conference on Data Mining series (ICDM), Dec 2011, Vancouver, Canada. pp.457-466 |
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VideoSense at TRECVID 2011 : Semantic Indexing from Light Similarity Functions-based Domain Adaptation with StackingTRECVID 2011 - TREC Video Retrieval Evaluation workshop, Nov 2011, Gaithersburg, MD, United States. 6p |
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Adaptation de domaine parcimonieuse par pondération de bonnes fonctions de similaritéConférence Francophone d'Apprentissage (CAp), May 2011, Chambéry, France. pp.295-310 |
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Sparse Domain Adaptation in a Good Similarity-Based Projection SpaceWorkshop at NIPS 2011: Domain Adaptation Workshop: Theory and Application, Dec 2011, Grenade, Spain |
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On the Usefulness of Similarity Based Projection Spaces for Transfer LearningFirst International Workshop on Similarity-Based Pattern Recognition, Sep 2011, Venise, Italy. pp.1-16 |
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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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A Spectral Approach for Probabilistic Grammatical Inference on Trees21st International Conference on Algorithmic Learning Theory (ALT 2010), Oct 2010, Australia. pp.74-88 |
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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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A Polynomial Algorithm for the Inference of Context Free Languages9th International Colloquium on Grammatical Inference - ICGI 2008, Sep 2008, St Malo, France. p.29-42 |
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SEDiL: Software for Edit Distance LearningEuropean Conference on Machine Learning (ECML 2008), Sep 2008, Belgium. pp.672-677 |
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Relevant Representations for the Inference of Rational Stochastic Tree LanguagesInternational Colloquium on Grammatical Inference, 2008, St Malo, France. pp.57-70 |
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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Learning rational stochastic tree languagesProceedings of the 18th International Conference on Algorithmic Learning Theory (ALT'07), 2007, Japan. p.242-256 |
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On Probability Distributions for Trees: Representations, Inference and LearningNIPS Workshop on Representations and Inference on Probability Distributions, Dec 2007, Whistler, Canada |
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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 |
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Using Pseudo-Stochastic Rational Languages in Probabilistic Grammatical Inference8th International Colloquium on Grammatical Inference (ICGI'06), 2006, Japan. p.112-124 |
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Learning Multipicity Tree AutomataICGI 2006, 2006, TOkyo, Japan. p.268-280 |
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Learning Stochastic Tree Edit Distance17th European Conference on Machine Learning, Sep 2006, Berlin, Germany. pp.42-53 |
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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 |
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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Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting2019 |
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Dimension-free Concentration Bounds on Hankel Matrices for Spectral Learning2013 |
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Flexible Domain Adaptation for Multimedia Indexing2011 |
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Learning rational stochastic languages2006 |
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Combining Users Feedback as a Source of Knowledge for Feature Selection in RegressionHubert Curien Laboratory, UMR 5516 CNRS. 2018 |
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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 |
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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 |
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A Survey on Metric Learning for Feature Vectors and Structured Data[Research Report] Laboratoire Hubert Curien UMR 5516. 2013 |
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PAC-Bayesian Majority Vote for Late Classifier Fusion[Research Report] LIF Marseille; LaHC Saint-Etienne. 2012 |