Mathieu Serrurier
Publications
Publications
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Efficient Robust Conformal Prediction via Lipschitz-Bounded NetworksForty-Second International Conference on Machine Learning (ICML 2025), Jul 2025, Vancouver, Canada |
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An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN ArchitecturesInternational Conference on Machine Learning, 2025, Vancouver, Canada |
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Orthogonium : A Unified, Efficient Library of Orthogonal and 1-Lipschitz Building BlocksICML 2025 Workshop on Championing Open- source Development in Machine Learning (CODEML ’25), Jul 2025, Vancouver, France |
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DP-SGD Without Clipping: The Lipschitz Neural Network WayICLR 2024 - 12th International Conference on Learning Representations, May 2024, Vienna (Austria), Austria |
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On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport PerspectiveConference on Neural Information Processing Systems (NeurIPS), Neural Information Processing Systems Foundation, Dec 2023, New Orleans (Louisiana), United States |
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Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks40th International Conference on Machine Learning, Jul 2023, Honolulu, Hawaii, United States. pp.2245-2271, ⟨10.5555/3618408.3618504⟩ |
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Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networksAdvances in Neural Information Processing Systems, Nov 2022, New Orleans, United States. ⟨10.48550/arXiv.2104.05097⟩ |
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Achieving robustness in classification using optimal transport with hinge regularizationConference on Computer Vision and Pattern Recognition, Jun 2021, Virtuel, Canada. ⟨10.1109/CVPR46437.2021.00057⟩ |
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Learning Disentangled Representations via Mutual Information Estimation16th European Conference on Computer Vision - ECCV 2020, Aug 2020, online, France. pp.205-221, ⟨10.1007/978-3-030-58542-6_13⟩ |
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Analogy in recommendation. Numerical vs. ordinal: A discussionIEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016) in IEEE World Congress on Computational Intelligence - WCCI 2016, Jul 2016, Vancouver, Canada. pp. 2220-2226 |
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Analogical Classifiers: A Theoretical PerspectiveEuropean Conference on Artificial Intelligence (ECAI 2016), Aug 2016, La Hague, France. pp. 689-697 |
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Entropy evaluation based on confidence intervals of frequency estimates : Application to the learning of decision trees32nd International Conference on Machine Learning (ICML 2015), Jul 2015, Lille, France. pp. 1576-1584 |
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Social Specialization of Space: Clustering Households on the French RivieraGenetic and Evolutionary Computation Conference, Jul 2015, Madrid, Spain. ⟨10.1145/2739482.2764665⟩ |
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Visualization of Frequent Itemsets with Nested Circular Layout and Bundling AlgorithmInternational Symposium on Visual Computing - ISVC 2013, Jul 2013, Rethymnon, Greece. pp.396-405, ⟨10.1007/978-3-642-41939-3_38⟩ |
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From Visualization to Association Rules : an automatic approachSCCG 2013, Spring Conference on Computer Graphics, ACM, May 2013, Smolenice, Slovakia. pp 57-64, ⟨10.1145/2508244.2508252⟩ |
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A scalable learning algorithm for Kernel Probabilistic Classifier7th International Conference on Scalable Uncertainty Management (SUM), Sep 2013, Washington, DC, United States. pp. 297-310 |
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Mining aeronautical data by using visualized driven rules extraction approachISIATM 2013, 2nd International Conference on Interdisciplinary Science for Innovative Air Traffic Management, Jul 2013, Toulouse, France |
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Statistical prediction of aircraft trajectory : regression methods vs point-mass modelATM 2013, 10th USA/Europe Air Traffic Management Research and Development Seminar, Jun 2013, Chicago, United States. pp xxxx |
A Possibilistic Rule-Based Classifier14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2012), Jul 2012, Catania, Italy. pp.21--31, ⟨10.1007/978-3-642-31709-5_3⟩ |
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Representing uncertainty by possibility distributions encoding confidence bands, tolerance and prediction intervalsSUM 2012, 6th International Conference on Scalable Uncertainty Management, Sep 2012, Marburg, Germany. pp 233-246, ⟨10.1007/978-3-642-33362-0_18⟩ |
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Simultaneous interval regression for K-nearest neighborAI 2012, 25th Australasian Joint Conference on Artificial Intelligence, Dec 2012, Sydney, Australia. pp 602-613, ⟨10.1007/978-3-642-35101-3_51⟩ |
Classification Based on Possibilistic Likelihood14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2012), Jul 2012, Catania, Italy. pp.420--429 |
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Possibilistic KNN regression using tolerance intervalsIPMU 2012, 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jul 2012, Catania, Italy. pp 410-419, ⟨10.1007/978-3-642-31718-7_43⟩ |
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Building possibility distribution based on confidence intervals of parameters of Gaussian mixturesSUM 2011, 5th International Conference on Scalable Uncertainty Management, Oct 2011, Dayton, United States |
Possibilistic Classifiers for Uncertain Numerical Data11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2011), Jun 2011, Belfast, Ireland. pp.434-446, ⟨10.1007/978-3-642-22152-1_37⟩ |
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Imprecise Regression Based on Possibilistic Likelihood5th International Conference on Scalable Uncertainty Management (SUM 2011), Oct 2011, Dayton, OH, United States. pp.447-459, ⟨10.1007/978-3-642-23963-2_35⟩ |
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Ground-based prediction of aircraft climb : point-mass model vs regression methodsComplex World 2011, 1st Annual Complex World Conference, Jul 2011, Seville, Spain. pp xxxx |
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Utilisation d'outils de Visual Data Mining pour l'exploration d'un ensemble de règles d'associationIHM 2011, 23ème Conférence Francophone sur l'Interaction Homme-Machine, Oct 2011, Nice, France. pp.Article N° 12, ⟨10.1145/2044354.2044369⟩ |
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Maximum-likelihood principle for possibility distributions viewed as families of probabilitiesIEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), Jun 2011, Taipei, Taiwan. pp.2987--2993, ⟨10.1109/FUZZY.2011.6007527⟩ |
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An automatic generation of metro-like maps to display flight routes for air traffic controllers: structure and color optimizationAVI 2010, International Working Conference on Advanced Visual Interfaces, May 2010, Roma, Italy. pp 233-240, ⟨10.1145/1842993.1843034⟩ |
From Bayesian classifiers to possibilistic classifiers for numerical data4th International Conference on Scalable Uncertainty Management (SUM 2010), Sep 2010, Toulouse, France. pp.112-125, ⟨10.1007/978-3-642-15951-0_15⟩ |
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Why Imprecise regression: a discussion5th International Conference on Soft Methods in Probability and Statistics (SMPS 2010), Sep 2010, Oviedo and Mieres, Asturias, Spain. pp.527-535, ⟨10.1007/978-3-642-14746-3_65⟩ |
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Increasing Air Traffic Control simulations realism through voice transformationAudioMostly 2009, Conference on Interaction with Sound, Sep 2009, Glasgow, United Kingdom |
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Génération et placement de couleurs sur une vue de type métroIHM 2009, 21ème Conférence Francophone sur l'Interaction Homme-Machine, Oct 2009, Grenoble, France. pp 55-58, ⟨10.1145/1629826.1629835⟩ |
Elicitation of Sugeno integrals : A version space learning perspectiveProc. 18th Int. Symp. on Methodologies for Intelligent Systems, ISMIS'09, Sep 2009, Prague, Czech Republic, Czech Republic. pp.392-401, ⟨10.1007/978-3-642-04125-9_42⟩ |
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Elicitating Sugeno Integrals: Methodology and a Case Study10th European Conference Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2009), Jul 2009, Verona, Italy. pp.712-723, ⟨10.1007/978-3-642-02906-6_61⟩ |
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Making regression imprecise for providing a better representation of precise dataSecond workshop of the ERCIM working group on Computing ans Statistics (ERCIM 2009), Oct 2009, Limassol, Cyprus |
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Arguing and explaining classifications4th International Workshop on Argumentation in Multi-Agent Systems (ArgMAS 2007), May 2007, Honolulu, HI, United States. pp.164--177, ⟨10.1007/978-3-540-78915-4_11⟩ |
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A general framework for imprecise regressionIEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2007), Jul 2007, London, United Kingdom. pp.1597--1602, ⟨10.1109/FUZZY.2007.4295605⟩ |
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Super increasing sequences and learning of default rules11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2006), Jul 2006, Paris, France |
Gérer les exceptions en programmation logique inductive multiclasse avec la logique possibiliste15ème Congrès Francophone de Reconnaissance des Formes et Intelligence Artificielle (RFIA 2006), Jan 2006, Tours, France. pp.(en ligne) |
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Version Space Learning for Possibilistic Hypotheses17th European Conference on Artificial Intelligence (ECAI 2006), European Coordinating Committee for Artificial Intelligence (ECCAI); Italian Association of Artificial Intelligence, Aug 2006, Riva del Garda, Italy. pp.801-802 |
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An Argumentation Framework for Concept LearningInternational Workshop on Non-Monotonic Reasoning (NMR 2006 @ KR 2006), Clausthal University of Technology, May 2006, Lake District, United Kingdom |
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Imprecise regression and regression on fuzzy data A preliminary discussion2006 IEEE International Conference on Fuzzy Systems, Jul 2006, Vancouver, Canada. ⟨10.1109/FUZZY.2006.1681908⟩ |
Fuzzy inductive logic programming: learning fuzzy rules with their implication14th IEEE International Conference on Fuzzy Systems, May 2005, Reno, Nevada, United States. pp.613-618, ⟨10.1109/FUZZY.2005.1452464⟩ |
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Possibilistic inductive logic programming8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2005), Jul 2005, Barcelone, Spain. pp.675-686 |
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Enrichir la programmation logique inductive avec des prédicats flous14ème Congrés Francophone AFRIF-AFIA de Reconnaissance des Formes et d'Intelligence Artificielle (RFIA 2004), Association française pour la reconnaissance et l'interprétation des formes; Association française pour l'intelligence artificielle, Jan 2004, Toulouse, France. pp.319-327 |
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Getting adaptability or expressivity in inductive logic programming by using fuzzy predicatesIEEE International Conference on Fuzzy Systems 2004, Jul 2004, Budapest, Hungary. pp.73-77, ⟨10.1109/FUZZY.2004.1375691⟩ |
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Learning first order fuzzy rules with their implication operator10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2004), Jul 2004, Perugia, Italy |
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A simulated annealing framework for ILP14th International Conference on Inductive Logic Programming (ILP 2004), 2004, Porto, Portugal. pp.288-304, ⟨10.1007/978-3-540-30109-7_22⟩ |
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Les avantages de la discrétisation floue dans l'apprentissage de règlesConférence sur l'Apprentissage Automatique (CAP 2004), LIRMM: Laboratoire d'informatique, de robotique et de microélectronique de Montpellier, Jun 2004, Montpellier, France |
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Learning first order fuzzy logic rules10th International Fuzzy Systems Association World Congress (IFSA 2003), Jun 2003, Istanbul, Turkey. pp.702--709, ⟨10.1007/3-540-44967-1_84⟩ |
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Apprentissage du type de règle floue le plus pertinentRencontres Francophones sur la Logique Floue et ses Applications (LFA 2003), Nov 2003, Tours, France. pp.81-88 |
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On the induction of different kinds of first-order fuzzy rules7th European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU 2003), Jul 2003, Aalborg, Denmark. pp.370-381, ⟨10.1007/978-3-540-45062-7_30⟩ |
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Using simulated annealing in ILP13th International Conference on Inductive Logic Programming (ILP 2003), Sep 2003, Szeged, Hungary |
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Enriching relational learning with fuzzy predicates7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2003), 2003, Cavtat-Dubrovnik, Croatia. pp.399-410, ⟨10.1007/978-3-540-39804-2_36⟩ |
Stratified induction in possibilistic logic9ème Conférence Internationale sur le traitement d'information et gestion d'incertitudes dans les systèmes à base de connaissances (IPMU 2002), Jun 2002, Annecy, France. pp.617-623 |
Coping with exceptions in multiclass ILP problems using possibilistic logic (Poster paper)Nineteenth International Joint Conference on Artificial Intelligence (IJCAI 2005), Jul 2005, Edinburgh, Scotland, United Kingdom. Gallus, IJCAI-05, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, UK, July 30-August 5, 2005, pp.1761-1762, 2005 |
Report on CIMI Thematic Trimester: Machine Learning[Rapport de recherche] IRIT : Institut de Recherche en Informatique de Toulouse. 2016 |