Philippe Leray
161
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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
Conference papers
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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
Conference papers
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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⟩
Conference papers
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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⟩
Conference papers
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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
Conference papers
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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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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⟩
Conference papers
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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⟩
Conference papers
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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⟩
Conference papers
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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⟩
Conference papers
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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⟩
Conference papers
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Relational Constraints for Metric Learning on Relational DataEighth International Workshop on Statistical Relational AI, IJCAI, Jul 2018, Stockholm, Sweden
Conference papers
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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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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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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⟩
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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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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
Conference papers
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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
Conference papers
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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
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Possibilistic networks parameter learning: Preliminary empirical comparison8èmes journées francophones de réseaux bayésiens (JFRB 2016), 2016, Clermont-Ferrand, France. pp.?-?
Conference papers
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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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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⟩
Conference papers
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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⟩
Conference papers
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Impact du choix de la méthode de partitionnement pour les forêts d'arbres latentsSFC2015, P. Kuntz, Sep 2015, Nantes, France. pp.24-27
Conference papers
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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
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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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Probabilistic Relational Models with Clustering UncertaintyIEEE International Joint Conference on Neural Networks (IJCNN 2015), Jul 2015, Killarney, Ireland
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On the equivalence between regularized NMF and similarity-augmented graph partitioningEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium
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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⟩
Conference papers
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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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Advances in Learning with Bayesian Networks6th International Conference on Agents and Artificial Intelligence (ICAART 2014), Mar 2014, Angers, 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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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
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Learning Probabilistic Relational Models using Non-Negative Matrix FactorizationThe 27th International FLAIRS Conference, Uncertain Reasoning Special Track, May 2014, Pensacola Beach, Florida, United States. pp.? - ?
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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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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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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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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⟩
Conference papers
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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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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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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
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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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Probabilistic cognitive mapsSeptièmes Journées de l'Intelligence Artificielle Fondamentale (JIAF), 2013, Aix en provence, France
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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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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⟩
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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⟩
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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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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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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
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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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Approximation efficace de mélanges bootstrap d'arbres de markov pour l'estimation de densité.Conférence francophone sur l'Apprentissage Automatique, 2012, Nancy, France. pp.16
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Summarizing and visualizing a set of bayesian networks with quasi essential graphsASMDA 2011, 2011, Roma, Italy. pp.1062-1069
Conference papers
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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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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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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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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
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SemCaDo: a serendipitous causal discovery algorithm for ontology evolutionThe IJCAI-11 Workshop on Automated Reasoning about Context and Ontology Evolution, Jul 2011, Barcelone, Spain
Conference papers
hal-00596261v1
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Ontology-based generation of object oriented bayesian networksBMAW 2011, 2011, Barcelona, Spain. pp.9-17
Conference papers
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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⟩
Conference papers
hal-00645003v1
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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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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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Semcado: a serendipitous causal discovery algorithm for ontology evolutionARCOE 2011, 2011, Barcelona, Spain. pp.43-47
Conference papers
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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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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
Conference papers
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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
Conference papers
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Sub-quadratic markov tree mixture models for probability density estimationCOMPSTAT 2010, 2010, Paris, France. pp.?-?
Conference papers
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Apprentissage de réseaux bayésiens hiérarchiques latents pour les études d'association pangénomiques5èmes 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
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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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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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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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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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hal-00474295v1
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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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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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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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hal-00656723v1
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Vers un apprentissage subquadratique pour les mélanges d'arbres5èmes Journées Francophones sur les Réseaux Bayésiens (JFRB2010), May 2010, Nantes, France
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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
Conference papers
hal-00467656v1
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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
Conference papers
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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 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
Conference papers
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Probability density estimation by perturbing and combining tree structured markov networksECSQARU 2009, 2009, Verona, Italy. pp.156-167
Conference papers
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Probability density estimation by perturbing and combining tree structured markov networksCAp 2009, 2009, Hammamet, Tunisia. pp.65-79
Conference papers
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Integrating ontological knowledge for iterative causal discovery and vizualisationWorkshop on Machine Learning and Visualization, 2009, Hammamet, Tunisia
Conference papers
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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
Conference papers
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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⟩
Conference papers
hal-00412286v1
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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
Conference papers
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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
Conference papers
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Estimation de densité par ensembles aléatoires de poly-arbresJournées Francophone sur les Réseaux Bayésiens, May 2008, Lyon, France
Conference papers
hal-00259868v1
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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
Conference papers
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UnCaDo: Unsure Causal DiscoveryJournées Francophone sur les Réseaux Bayésiens, May 2008, Lyon, France
Conference papers
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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
Conference papers
hal-00259009v1
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Specific graphical models for analyzing the reliabilityMED'08, 2008, Ajaccio, France. pp.621-626
Conference papers
hal-00412495v1
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Density estimation with ensembles of randomized poly-treesBENELEARN 2008, May 2008, Spa, Belgium. pp.31-32
Conference papers
hal-00568050v1
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Reliability analysis using graphical duration modelsARES 2008, 2008, Barcelona, Spain. pp.795-800
Conference papers
hal-00412299v1
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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
Conference papers
hal-00412885v1
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Dynamic bayesian networks modelling maintenance strategies: Prevention of broken railsWCCR'08, 2008, Seoul, South Korea
Conference papers
hal-00412291v1
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High-dimensional probability density estimation with randomized ensembles of tree structured bayesian networksPGM 2008, 2008, Hirtshals, Denmark. pp.9-16
Conference papers
hal-00412288v1
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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⟩
Conference papers
hal-00412946v1
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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
Conference papers
hal-00412501v1
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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⟩
Conference papers
hal-00412943v1
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Generation of incomplete test-data using bayesian networksIJCNN, 2007, Orlando, United States. pp.2391-2396, ⟨10.1109/IJCNN.2007.4371332⟩
Conference papers
hal-00412939v1
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Dynamic Compact Thermal Model with Neural Networks for Radar ApplicationsTHERMINIC 2006, Sep 2006, Nice, France. pp.118-122
Conference papers
hal-00171366v1
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Pertinence des mesures de confiance en classificationConférence francophone RFIA, Feb 2000, Paris, France
Conference papers
hal-01573394v1
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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
Conference papers
hal-00272421v1
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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⟩
Conference papers
hal-01617480v1
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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
Conference papers
hal-01624733v1
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Neural Networks for Alarm Generation in Telephone ManagementEighth Workshop on Principles of Diagnostic, 1997, Mont Saint-Michel, France
Conference papers
hal-01649027v1
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Revue d'Intelligence Artificielle VOL 21/3 - 2007 - numéro spécial Modèles graphiques probabilistesHermes, pp.157, 2007
Books
hal-00412919v1
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Réseaux bayésiensEyrolles, pp.424, 2007, Algorithmes
Books
hal-00412267v1
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Réseaux BayésiensEyrolles, pp.224, 2004, Algorithmes
Books
hal-00412269v1
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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⟩
Book sections
hal-02049801v1
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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⟩
Book sections
hal-01205961v1
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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
Book sections
hal-01204956v1
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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
Book sections
hal-01020910v1
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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⟩
Book sections
hal-00915532v1
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A dynamic graphical model to represent complex survival distributionsAdvances in Mathematical Modeling for Reliability, IOS Press, pp.17-24, 2008
Book sections
hal-00412259v1
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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⟩
Book sections
hal-00412263v1
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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
Book sections
hal-00412264v1
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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⟩
Other publications
hal-00864163v1
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Forests of hierarchical latent models for association genetics2010
Other publications
hal-00503013v1
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GWAS-AS: assistance for a thorough evaluation of advanced algorithms dedicated to genome-wide association studies2010
Other publications
hal-00915535v1
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Learning a forest of Hierarchical Bayesian Networks to model dependencies between genetic markers2010
Preprints, Working Papers, ...
hal-00444087v2
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Réseaux bayésiens : Apprentissage et diagnostic de systemes complexesModélisation et simulation. Université de Rouen, 2006
Habilitation à diriger des recherches
tel-00485862v1
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