Philippe Leray
Publications
Publications
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Mining Discriminative Sequential Patterns of Self-regulated LearnersITS 2024: 20th International Conference on Intelligent Tutoring Systems, Jun 2024, Thessalonique, Greece. pp.137-149, ⟨10.1007/978-3-031-63031-6_12⟩ |
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Une extension des modèles graphiques de durée pour estimer l'évolution des coûts de maintenance dans le logement résidentiel11èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes, Nantes Université - pôle Sciences et technologie, Jun 2023, Nantes, France |
An optimized Quantum circuit representation of Bayesian networks11èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes, Nantes Université - pôle Sciences et technologie, Jun 2023, Nantes, France |
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Un modèle générique avec structuration des compétences et facteurs externes pour le Bayesian Knowledge Tracing11èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes, Nantes Université - pôle Sciences et technologie, Jun 2023, Nantes,, France |
L’autorégulation des apprentissages dans une formation pour adulte. L’exemple de la demande d’aideCongrès international d’Actualité de la Recherche en Éducation et en Formation (AREF), Sep 2022, Lausanne, Suisse |
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Unsupervised co-training of bayesian networks for condition prediction34th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, 2021, Kuala Lumpur, Malaysia. pp.577-588, ⟨10.1007/978-3-030-79463-7_49⟩ |
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Supporting Self-Regulation Learning Using a Bayesian Approach. Some Preliminary InsightsInternational Joint Conference on Artificial Intelligence IJCAI-21, Workshop Artificial Intelligence for Education, Aug 2021, Montreal (virtual), Canada |
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Unsupervised condition monitoring with bayesian networks: an application on high speed machining31th European Safety and Reliability Conference, ESREL 2021, 2021, Angers, France. pp.1990-1997, ⟨10.1007/978-3-030-86772-0_16⟩ |
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Interactive anomaly detection in mixed tabular data using Bayesian networks10th International Conference on Probabilistic Graphical Models (PGM 2020), Sep 2020, Aalborg, Denmark |
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Graphical event model learning and verification for security assessment32th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2019), 2019, Graz, Austria. pp.245-252, ⟨10.1007/978-3-030-22999-3_22⟩ |
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On intercausal interactions in probabilistic relational modelsThe Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA ’19), 2019, Ghent, Belgium. pp.327 - 329 |
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Multi-task transfer learning for timescale graphical event models15th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2019), 2019, Belgrade, Serbia. ⟨10.1007/978-3-030-29765-7_26⟩ |
A probabilistic relational model approach for fault tree modeling with spatial information and resource management32th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2019), 2019, Graz, Austria. pp.555-563, ⟨10.1007/978-3-030-22999-3_48⟩ |
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Relational Constraints for Metric Learning on Relational DataEighth International Workshop on Statistical Relational AI, IJCAI, Jul 2018, Stockholm, Sweden |
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Qualitative probabilistic relational modelsThe 12th International Conference on Scalable Uncertainty Management (SUM 2018), 2018, Milano, Italy. pp.276-289, ⟨10.1007/978-3-030-00461-3_19⟩ |
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Using Probabilistic Relational Models to Generate Synthetic Spatial or Non-spatial DatabasesResearch Challenges in Information Science (RCIS) 2018, 12th International Conference on, May 2018, Nantes, France. pp.1-12, ⟨10.1109/RCIS.2018.8406645⟩ |
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Complex event processing under uncertainty using Markov chains, constraints, and sampling2nd International Joint Conference on Rules and Reasoning (RuleML+RR 2018), 2018, Luxembourg, Luxembourg. pp.147-163, ⟨10.1007/978-3-319-99906-7_10⟩ |
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DAPER joint learning from partially structured Graph DatabasesThird annual International Conference on Digital Economy (ICDEc 2018), 2018, Brest, France. pp.129-138, ⟨10.1007/978-3-319-97749-2_10⟩ |
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A probabilistic relational model approach for fault trees modeling30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2017), 2017, Arras, France. ⟨10.1007/978-3-319-60045-1_18⟩ |
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Customer relationship management and small data - application of bayesian network elicitation techniques for building a lead scoring model14th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2017), Oct 2017, Hammamet, Tunisia. pp.251-255, ⟨10.1109/AICCSA.2017.51⟩ |
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Learning the parameters of possibilistic networks from data: Empirical comparisonThirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 30), 2017, Marco Island, United States |
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On the use of walkSAT based algorithms for MLN inference in some realistic applications30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2017), 2017, Arras, France. ⟨10.1007/978-3-319-60045-1_15⟩ |
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Learning probabilistic relational models with (partially structured) graph databases14th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2017), 2017, Hammamet, Tunisia. ⟨10.1109/AICCSA.2017.39⟩ |
Possibilistic MDL: a new possibilistic likelihood based score function for imprecise dataFourteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2017), 2017, Lugano, Switzerland. pp.435-445, ⟨10.1007/978-3-319-61581-3_39⟩ |
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Détection et prédiction de défaillances dans un parc d'éoliennes à l'aide de réseaux bayésiens8èmes journées francophones de réseaux bayésiens (JFRB 2016), 2016, Clermont-Ferrand, France |
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A hybrid approach for probabilistic relational models structure learning15th International Symposium on Intelligent Data Analysis (IDA 2016), 2016, Stockholm, Sweden. ⟨10.1007/978-3-319-46349-0_4⟩ |
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Possibilistic networks parameter learning: Preliminary empirical comparison8èmes journées francophones de réseaux bayésiens (JFRB 2016), 2016, Clermont-Ferrand, France. pp.?-? |
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An exact approach to learning probabilistic relational model8th International Conference on Probabilistic Graphical Models (PGM 2016), 2016, Lugano, Switzerland. pp.171-182 |
État de l’art des méthodes de détections de communautés dans les réseaux bipartis binaires et pondérés.In 7ème édition du colloque bisannuel Apprentissage Artificiel & Fouille de Données (AAFD) et 23èmes Rencontres annuelles de la Société Francophone de Classification (SFC), 2016, Marrakech, Maroc. pp.1-6 |
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Evaluating product-based possibilistic networks learning algorithms13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2015), 2015, Compiègne, France. ⟨10.1007/978-3-319-20807-7_28⟩ |
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CPD tree learning using contexts as background knowledge13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2015), 2015, Compiègne, France. ⟨10.1007/978-3-319-20807-7_32⟩ |
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Learning possibilistic networks from data: a survey.16th World Congress of the International Fuzzy Systems Association (IFSA) and the 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), 2015, Gijon, Spain. ⟨10.2991/ifsa-eusflat-15.2015.30⟩ |
Probabilistic Relational Models with Clustering UncertaintyIEEE International Joint Conference on Neural Networks (IJCNN 2015), Jul 2015, Killarney, Ireland |
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Integrating spatial information into probabilistic relational model2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015), 2015, Paris, France. ⟨10.1109/DSAA.2015.7344800⟩ |
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On the equivalence between regularized nmf and similarity-augmented graph partitioning23th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015), 2015, Bruges, Belgium |
Impact du choix de la méthode de partitionnement pour les forêts d'arbres latentsSFC2015, P. Kuntz, Sep 2015, Nantes, France. pp.24-27 |
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Modeling genetical data with forests of latent trees for applications in association genetics at a large scale. Which clustering method should be chosen?International Conference on Bioinformatics Models, Methods and Algorithms, Bioinformatics2015, Nov 2014, Lisbon, Portugal. pp.12 |
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Learning Probabilistic Relational Models Using Non-Negative Matrix FactorizationInternational Florida Artificial Intelligence Research Society (FlAIRS) Conference, May 2014, Pensacola Beach, Floride, United States |
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New data mining techniques in materials science: Bayesian networks to predict the yield stress of Ni-base superalloysTMS2014, 143rd Annual Meeting & Exhibition, 2014, San Diego, United States |
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La génération aléatoire de réseaux bayésiens relationnels7ème journées francophones sur les réseaux bayésiens (JFRB 2014), 2014, Paris, France |
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Random generation and population of probabilistic relational models and databases26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2014), Nov 2014, Limassol, Cyprus. pp.756-763, ⟨10.1109/ICTAI.2014.117⟩ |
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Apprentissage de modèles relationnels probabilistes par factorisation non-négative de matrices7èmes journées francophones sur les réseaux bayésiens (JFRB 2014), 2014, Paris, France |
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Probabilistic Cognitive Maps Semantics of a Cognitive Map when the Values are Assumed to be ProbabilitiesInternational Conference on Agents and Artificial Intelligence (ICAART), 2014, Angers, France. pp.52-62 |
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Évaluation des algorithmes d'apprentissage de structure pour les réseaux bayésiens dynamiques.7èmes journées francophones sur les réseaux bayésiens (JFRB 2014), 2014, Paris, France |
Relational bayesian networks for recommender systems: review and comparative studyENBIS-SFdS Spring Meeting on graphical causality models: Trees, Bayesian Networks and Big Data, Apr 2014, Paris, France |
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Apprentissage des réseaux possibilistes à partir de données: un survol7èmes journées francophones sur les réseaux bayésiens (JFRB 2014), 2014, Paris, France |
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Advances in Learning with Bayesian Networks6th International Conference on Agents and Artificial Intelligence (ICAART 2014), Mar 2014, Angers, France |
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A Personalized Recommender System from Probabilistic Relational Model and Users’ PreferencesKnowledge-Based and Intelligent Information & Engineering Systems 18th Annual Conference, KES-2014, Sep 2014, Gdynia, Poland. pp.1063-1072, ⟨10.1016/j.procs.2014.08.193⟩ |
Bayesian networks in materials science: new tools to predict the properties of materialsTMS2014 - 143rd Annual Meeting & Exhibition, 2014, San Diego, United States |
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Probabilistic cognitive mapsSeptièmes Journées de l'Intelligence Artificielle Fondamentale (JIAF), 2013, Aix en provence, France |
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Learning Probabilistic Relational Models using co-clustering methodsStructured Learning: Inferring Graphs from Structured and Unstructured Inputs (SLG 2013) ICML Workshop, 2013, Atlanta, United States |
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A RBN-based recommender system architectureInternational Conference on Modeling, Simulation and Applied Optimization (ICMSAO 2013), 2013, Hammamet, Tunisia. pp.1-6, ⟨10.1109/ICMSAO.2013.6552609⟩ |
Imputation of possibilistic data for structural learning of directed acyclic graphs Genova, Italy.International Workshop on Fuzzy Logic and Applications, 2013, Genoa, Italy. pp.68-76, ⟨10.1007/978-3-319-03200-9_8⟩ |
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Modeling of genotype data with forests of latent trees to detect genetic causes of diseasesAdo2013 (Machine Learning and Omics Data), Dec 2013, Lille, France. 6 p |
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Active learning of causal bayesian networks using ontologies: a case studyInternational Joint Conference on Neural Networks, 2013, Dallas, United States. pp.1-6 |
Dynamic MMHC: a local search algorithm for dynamic bayesian network structure learningInternational Symposium on Intelligent Data Analysis, 2013, London, United Kingdom. pp.392-403, ⟨10.1007/978-3-642-41398-8_34⟩ |
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Incremental bayesian network structure learning in high dimensional domainsInternational Conference on Modeling, Simulation and Applied Optimization (ICMSAO 2013), 2013, Hammamet, Tunisia. pp.1-6, ⟨10.1109/ICMSAO.2013.6552635⟩ |
Benchmarking dynamic bayesian network structure learning algorithmsInternational Conference on Modeling, Simulation and Applied Optimization (ICMSAO 2013), 2013, Hammamet, Tunisia. pp.1-6, ⟨10.1109/ICMSAO.2013.6552549⟩ |
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Discrete exponential bayesian networks structure learning for density estimationInternational Conference on Intelligent Computing, 2012, Huangshan, China. pp.?-?, ⟨10.1007/978-3-642-31837-5_21⟩ |
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Forests of latent tree models for the detection of genetic associationsInternational Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2012), Feb 2012, Vilamoura, Portugal. pp.1-10, ⟨10.5220/0003703400050014⟩ |
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Approximation efficace de mélanges bootstrap d'arbres de Markov pour l'estimation de densitéConférence Francophone sur l'Apprentissage Automatique - CAp 2012, Laurent Bougrain, May 2012, Nancy, France. 16 p |
A new implicit parameter estimation for conditional gaussian bayesian networksUncertainty Modeling in Knowledge Engineering and Decision Making, 2012, Istanbul, Turkey. pp.?-? |
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Local Skeleton Discovery for Incremental Bayesian Network Structure LearningInternational Conference on Computer Networks and Information Technology (ICCNIT), Jul 2011, Peshawar, Pakistan |
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Efficiently approximating markov tree bagging for high-dimensional density estimationECML-PKDD 2011, 2011, Athens, Greece. pp.113-128, ⟨10.1007/978-3-642-23808-6_8⟩ |
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Mixture of markov trees for bayesian network structure learning with small datasets in high dimensional spaceECSQARU 2011, 2011, Belfast, Ireland. pp.229-238 |
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Summarizing and visualizing a set of bayesian networks with quasi essential graphsASMDA 2011, 2011, Roma, Italy. pp.1062-1069 |
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SemCaDo: a serendipitous strategy for learning causal bayesian networks using ontologiesThe 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Jun 2011, Belfast, Ireland. pp.182-193 |
Multiple hypothesis testing and quasi essential graph for comparing two sets of bayesian networksKES 2011, 2011, Kaiserslautern, Germany. pp.176-185, ⟨10.1007/978-3-642-23863-5_18⟩ |
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Ontology-based generation of object oriented bayesian networksBMAW 2011, 2011, Barcelona, Spain. pp.9-17 |
Discrete exponential bayesian networks: an extension of bayesian networks to discrete natural exponential familiesICTAI 2011, 2011, Palm Beach County, United States. pp.?-?, ⟨10.1109/ICTAI.2011.38⟩ |
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immpc: A local search approach for incremental bayesian network structure learningIDA 2011, 2011, Porto, Portugal. pp.401-412, ⟨10.1007/978-3-642-24800-9_37⟩ |
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A two-way approach for probabilistic graphical models structure learning and ontology enrichment.KEOD 2011, 2011, Paris, France. pp.189-194 |
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Sub-quadratic markov tree mixture models for probability density estimationCOMPSTAT 2010, 2010, Paris, France. pp.?-? |
From redundant/irrelevant alert elimination to handling IDSs' reliability and controlling severe attack prediction/false alarm rate tradeoffsFifth Conference on Network and Information Systems Security (SARSSI 2010), May 2010, Nice, France. pp.15 |
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Handling IDS' reliability in alert correlation: A Bayesian network-based model for handling IDS's reliability and controlling prediction/false alarm rate tradeoffsInternational Conference on Security and Cryptography (SECRYPT'2010), Jul 2010, Athène, Greece. pp.11 |
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Learning Hierarchical Bayesian Networks for Genome-Wide Association StudiesCOMPSTAT, Nineteenth International Conference on Computational Statististics, Aug 2010, Paris, France. pp.549-556 |
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From redundant/irrelevant alert elimination to handling idss' reliability and controlling severe attack prediction/false alarm rate tradeoffs5th Conference on Network and Information Systems Security (SARSSI'10), 2010, Rocquebrune Cap-Martin, France |
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Apprentissage de réseaux bayésiens hiérarchiques latents pour les études d'association pangénomiquesProc. JFRB 2010, 5th French-speaking meeting on Bayesian networks, Nantes, May 2010, Nantes, France. pp.11-12 |
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Réseaux bayésiens hiérarchiques avec variables latentes pour la modélisation des dépendances entre SNP: une approche pour les études d'association pangénomiquesProc. SFC 2010, XVIIth Join Meeting of the French Society of Classification, France, Saint-Denis de la Réunion, 9-11 june, Jun 2010, Saint-Denis de la Réunion, France. pp.25-29 |
Handling idss' reliability in alert correlation: A bayesian network-based model for handling IDS's reliability and controlling prediction/false alarm rate tradeoffsInternational Conference on Security and Cryptography (SECRYPT'10), 2010, Athens, Greece. pp.14-24 |
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Mélanges sous-quadratiques d'arbres de Markov pour l'estimation de la densité de probabilité5èmes Journées Francophones sur les Réseaux Bayésiens (JFRB2010), May 2010, Nantes, France |
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Towards sub-quadratic learning of probability density models in the form of mixtures of treesESANN 2010, 2010, Bruges, Belgium. pp.219-224 |
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Vers un apprentissage subquadratique pour les mélanges d'arbres5èmes Journées Francophones sur les Réseaux Bayésiens (JFRB2010), May 2010, Nantes, France |
Hierarchical Bayesian networks applied to association geneticsMODGRAPH 2010 (Modèles graphiques probabilistes pour l'intégration de données hétérogènes et la découverte de modèles causaux en biologie), Journée satellite de JOBIM 2010, Sep 2010, Montpellier, France |
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Differential study of the cytokine network in the immune system: An evolutionary approach based on the Bayesian networksThe 2nd Asian Conference on Intelligent Information and Database Systems (ACIIDS), Mar 2010, Hue City, Vietnam. pp.?-? |
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Approches basées sur les réseaux Bayésiens pour la prédiction d'attaques sévères5èmes Journées Francophones sur les Réseaux Bayésiens (JFRB2010), May 2010, Nantes, France |
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L'intégration des connaissances ontologiques pour l'apprentissage des réseaux bayesiens causaux5èmes Journées Francophones sur les Réseaux Bayésiens (JFRB2010), May 2010, Nantes, France |
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Sub-quadratic Markov tree mixture learning based on randomizations of the Chow-Liu algorithmPGM 2010, Sep 2010, Helsinki, Finland. pp.17-25 |
Bayesian network-based approaches for severe attack prediction and handling IDSs' reliabilityInternational Conference on Information Processing and Management of Uncertainty (IPMU'10), Jun 2010, Dortmund, Germany. pp.12 |
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From redundant/irrelevant alert elimination to handling IDSes reliability and controlling severe attack prediction/false alarm rate tradeoffs5ème Conférence sur la sécurité des architectures réseaux et systèmes d'information, May 2010, Menton, France. pp.NC |
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Modélisation des dépendances locales entre SNP à l'aide d'un réseau bayésienProc. SFC'09, XVIth Join Meeting of the French Society of Classification, actes des 16èmes rencontres de la Société Francophone de Classification, Sep 2009, Grenoble, France. pp.169-172 |
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Probability density estimation by perturbing and combining tree structured markov networksECSQARU 2009, 2009, Verona, Italy. pp.156-167 |
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A Bayesian network approach to model local dependencies among SNPsMODGRAPH 2009 Probabilistic graphical models for integration of complex data and discovery of causal models in biology, satellite meeting of JOBIM 2009, Jun 2009, Nantes, France |
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Integrating ontological knowledge for iterative causal discovery and vizualisationECSQARU 2009, 2009, Verona, Italy. pp.168-179, ⟨10.1007/978-3-642-02906-6_16⟩ |
Integrating ontological knowledge for iterative causal discovery and vizualisationWorkshop on Machine Learning and Visualization, 2009, Hammamet, Tunisia |
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Probability density estimation by perturbing and combining tree structured markov networksCAp 2009, 2009, Hammamet, Tunisia. pp.65-79 |
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Approches markovienne et semi-markovienne pour la modélisation de la fiabilité et des actions de maintenance d'un système ferroviaireWorkshop Surveillance, Sûreté et Sécurité des Grands Systèmes (3SGS'08), 2008, Troyes, France |
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High-dimensional probability density estimation with randomized ensembles of tree structured bayesian networksPGM 2008, 2008, Hirtshals, Denmark. pp.9-16 |
Specific graphical models for analyzing the reliabilityMED'08, 2008, Ajaccio, France. pp.621-626 |
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Dynamic bayesian networks modelling maintenance strategies: Prevention of broken rails8th World Congress on Railway Research (WCRR 2008), May 2008, Seoul, South Korea |
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Density estimation with ensembles of randomized poly-treesBENELEARN 2008, May 2008, Spa, Belgium. pp.31-32 |
Reliability analysis using graphical duration modelsARES 2008, 2008, Barcelona, Spain. pp.795-800 |
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Estimation de densité par ensembles aléatoires de poly-arbresJournées Francophone sur les Réseaux Bayésiens, May 2008, Lyon, France |
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UnCaDo: Unsure Causal DiscoveryJournées Francophone sur les Réseaux Bayésiens, May 2008, Lyon, France |
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Approche semi-markovienne pour la modélisation de stratégies de maintenance: application à la prévention de rupture du railMOSIM'2008, 2008, Paris, France. pp.CDROM |
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Réseaux bayésiens dynamiques pour la représentation de modèles de durée en temps discretJournées Francophone sur les Réseaux Bayésiens, May 2008, Lyon, France |
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Evolutivité d'une architecture en temps réel de filtrage d'alertes générées par les systèmes de détection d'intrusions sur les réseauxRFIA 2008, 2008, Amiens, France. pp.CDROM |
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Causal graphical models with latent variables: Learning and inferenceECSQARU, 2007, Hammamet, Tunisia. pp.5-16, ⟨10.1007/978-3-540-75256-1_4⟩ |
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A generic approach to model complex system reliability using graphical duration modelsMathematical Methods in Reliability: Methodology and Practice (MMR 2007),, 2007, Glasgow, United Kingdom |
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Generation of incomplete test-data using bayesian networksIJCNN, 2007, Orlando, United States. pp.2391-2396, ⟨10.1109/IJCNN.2007.4371332⟩ |
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Growing hierarchical self-organizing map for alarm filtering in network intrusion detection systemsNTMS'07, 2007, Paris, France. pp.CDROM, ⟨10.1007/978-1-4020-6270-4_58⟩ |
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Dynamic Compact Thermal Model with Neural Networks for Radar ApplicationsTHERMINIC 2006, Sep 2006, Nice, France. pp.118-122 |
Pertinence des mesures de confiance en classificationConférence francophone RFIA, Feb 2000, Paris, France |
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Data Fusion for Diagnosis in a Telecommunication NetworkICANN 1998 - 8th International Conference of Artificial Neural Networks, Sep 1998, Skövde, Sweden. pp.767-772, ⟨10.1007/978-1-4471-1599-1_118⟩ |
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Analyse de séquences non calibrées pour la reconstruction 3D de scèneActes 11ème Congrès AFCET-RFIA (RFIA'98), Jan 1998, Clermont-Ferrand, France. pp.189-198 |
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Local diagnosis for real-time network traffic managementInternational Workshop on Applications of Neural Networks to Telecommunications (IWANNT'97), Jun 1997, Melbourne, Australia |
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Neural Networks for Alarm Generation in Telephone ManagementEighth Workshop on Principles of Diagnostic, 1997, Mont Saint-Michel, France |
Revue d'Intelligence Artificielle VOL 21/3 - 2007 - numéro spécial Modèles graphiques probabilistesHermes, pp.157, 2007 |
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Réseaux bayésiensEyrolles, pp.424, 2007, Algorithmes |
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Réseaux BayésiensEyrolles, pp.224, 2004, Algorithmes |
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Belief Graphical Models for Uncertainty representation and reasoningA Guided Tour of Artificial Intelligence Research, volume II: AI Algorithms, pp.209-246, 2020, ⟨10.1007/978-3-030-06167-8_8⟩ |
Latent Forests to Model Genetical Data for the Purpose of Multilocus Genome-wide Association Studies. Which clustering should be chosen?Communication in Computer and Information Science, Springer, pp.17, 2015, BIOSTEC2015 |
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A Probabilistic Semantics for Cognitive MapsAgents and Artificial Intelligence 6th International Conference, ICAART 2014, Angers, France, March 6-8, 2014, Revised Selected Papers, 8946, Springer, pp.151-169, 2015, Lecture Notes in Artificial Intelligence, ⟨10.1007/978-3-319-25210-0_10⟩ |
Modèles graphiques pour l'incertitude : inférence et apprentissageP. Marquis, O. Papini, H. Prade. Panorama de l'Intelligence Artificielle, volume 2: Algorithmes pour l'intelligence artificielle, Cepadues, 26 p., 2014, 9782364930414 |
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Forests of latent tree models to decipher genotype-phenotype associationsJ. Gariel, J. Schier, S. Van Huffel, E. Conchon, C. Correia, A. Fred and H. Gamboa. Biomedical Engineering Systems and Technologies, Communication in Computer and Information Science 357, Springer Berlin Heidelberg, pp.113-134, 2013, 978-3-642-38255-0. ⟨10.1007/978-3-642-38256-7_8⟩ |
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A dynamic graphical model to represent complex survival distributionsAdvances in Mathematical Modeling for Reliability, IOS Press, pp.17-24, 2008 |
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Causal graphical models with latent variables : learning and inferenceHolmes, D. E. and Jain, L. Innovations in Bayesian Networks: Theory and Applications, Springer, pp.219-249, 2008, Studies in Computational Intelligence, vol.156/2008, ⟨10.1007/978-3-540-85066-3_9⟩ |
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An integral approach to causal inference with latent variablesRusso, F. and Williamson, J. Causality and Probability in the Sciences, London College Publications, pp.17-41, 2007, Texts In Philosophy series |
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Editorial: Uncertainty in artificial intelligence and databases - International Journal of Approximate Reasoning, 54(7)2013, pp.825-826. ⟨10.1016/j.ijar.2013.04.001⟩ |
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Forests of hierarchical latent models for association genetics2010 |
GWAS-AS: assistance for a thorough evaluation of advanced algorithms dedicated to genome-wide association studies2010 |
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Learning a forest of Hierarchical Bayesian Networks to model dependencies between genetic markers2010 |
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A Framework for Offline Evaluation of Recommender Systems based on Probabilistic Relational Models[Technical Report] Laboratoire des Sciences du Numérique de Nantes; Capacités SAS. 2017 |
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Probabilistic Relational Model Benchmark Generation[Technical Report] LARODEC Laboratory, ISG, Université de Tunis, Tunisia; DUKe research group, LINA Laboratory UMR 6241, University of Nantes, France; DataForPeople, Nantes, France. 2016 |
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Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy[Research Report] Laboratoire d'Informatique de Nantes Atlantique. 2016 |
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Probabilistic Relational Models for Customer Preference Modelling and Recommendation[Research Report] Laboratoire d'Informatique de Nantes Atlantique. 2013 |
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On Evaluation of a Population of Bayesian Networks2012 |
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Benchmarking dynamic Bayesian network structure learning algorithms2012 |
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Feature Selection with Neural Networks[Research Report] lip6.1998.012, LIP6. 1998 |
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Réseaux bayésiens : Apprentissage et diagnostic de systemes complexesModélisation et simulation. Université de Rouen, 2006 |