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Philippe LERAY

ENSEIGNANT-CHERCHEUR

HDR

: philippe.Lerayatls2n.fr

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Publications référencées sur HAL

Revues internationales avec comité de lecture (ART_INT)

    • [1] S. Mouchabac, P. Leray, V. Adrien, F. Gollier-Briant, O. Bonnot. Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations. In Journal of Medical Internet Research ; éd. JMIR Publications, 2021, vol. 23, num. 9.
      https://hal.sorbonne-universite.fr/hal-03369481
    • [2] M. Ferhat, M. Ritou, P. Leray, N. Le Du. Incremental discovery of new defects: application to screwing process monitoring. In CIRP Annals - Manufacturing Technology ; éd. Elsevier, 2021, vol. 70, num. 1.
      https://hal.science/hal-03254677
    • [3] V. Asvatourian, P. Leray, S. Michiels, E. Lanoy. Integrating expert’s knowledge constraint of time dependent exposures in structure learning for Bayesian networks. In Artificial Intelligence in Medicine ; éd. Elsevier, 2020.
      https://hal.science/hal-02864601
    • [4] M. Ben Ishak, P. Leray, N. Ben Amor. Probabilistic relational model benchmark generation: Principle and application. In Intelligent Data Analysis ; éd. IOS Press, 2016, vol. 20, num. 3.
      https://hal.science/hal-01150688
    • [5] M. Ben Messaoud, P. Leray, N. Ben Amor. SemCaDo: A serendipitous strategy for causal discovery and ontology evolution. In Knowledge-Based Systems ; éd. Elsevier, 2015, vol. 76.
      https://hal.science/hal-01113245
    • [6] A. Jarraya, P. Leray, A. Masmoudi. Discrete exponential bayesian networks : definition, learning and application for density estimation. In Neurocomputing ; éd. Elsevier, 2014, vol. Volume 137.
      https://hal.science/hal-00864150
    • [7] A. Jarraya, P. Leray, A. Masmoudi. Implicit parameter estimation for conditional gaussian bayesian networks. In International Journal of Computational Intelligence Systems ; éd. Atlantis Press, 2014, vol. 7, num. Supplement 1.
      https://hal.science/hal-00864152
    • [8] R. Mourad, C. Sinoquet, N. Zhang, T. Liu, P. Leray. A survey on latent tree models and applications. In Journal of Artificial Intelligence Research ; éd. Association for the Advancement of Artificial Intelligence, 2013, vol. 47.
      https://hal.science/hal-00828445
    • [9] R. Mourad, C. Sinoquet, P. Leray. Probabilistic graphical models for genetic association studies. In Briefings in Bioinformatics ; éd. Oxford University Press (OUP), 2012, vol. 13, num. 1.
      https://hal.science/hal-00573325
    • [10] K. Tabia, P. Leray. Alert correlation: Severe attack prediction and controlling false alarm rate tradeoffs. In Intelligent Data Analysis ; éd. IOS Press, 2011, vol. 15, num. 6.
      https://hal.science/hal-00568027
    • [11] R. Mourad, C. Sinoquet, C. Dina, P. Leray. Visualization of pairwise and multilocus linkage disequilibrium structure using latent forests.. In PLoS ONE ; éd. Public Library of Science, 2011, vol. 6, num. 12.
      https://hal.science/hal-00655876
    • [12] R. Mourad, C. Sinoquet, P. Leray. A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies.. In BMC Bioinformatics ; éd. BioMed Central, 2011, vol. 12, num. 1.
      https://hal.science/hal-00567988
    • [13] R. Donat, P. Leray, L. Bouillaut, P. Aknin. A dynamic bayesian network to represent discrete duration models. In Neurocomputing ; éd. Elsevier, 2010, vol. 73, num. 4-6.
      https://hal.science/hal-00425443
    • [14] O. François, P. Leray. Apprentissage de structure des réseaux bayésiens et données incomplètes. In Revue des Nouvelles Technologies de l'Information ; éd. Editions RNTI, 2005, vol. RNTI-E-3.
      https://univ-eiffel.hal.science/hal-04228243
    • [15] O. François, P. Leray. Étude Comparative d’Algorithmes d’Apprentissage de Structure dans les Réseaux Bayésiens. In JEDAI - Journal électronique d'intelligence artificielle ; éd. AFIA, 2005.
      https://hal.science/hal-04227290
    • [16] P. Leray, P. Gallinari. Feature extraction with neural networks. In Behaviormetrika ; éd. Behaviormetric Society of Japan, 1999, vol. 26, num. 1.
      https://hal.science/hal-01184481

Revues nationales avec comité de lecture (ART_NAT)

    • [17] N. Ben Amor, M. Haddad, P. Leray. Apprentissage des réseaux possibilistes à partir de données: un survol. In Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle ; éd. Lavoisier, 2015, vol. 29, num. 2.
      https://hal.science/hal-01166897
    • [18] S. Meganck, P. Leray, B. Manderick. Causal discovery in non-ideal frameworks. Information interaction intelligence. In Revue I3 - Information Interaction Intelligence ; éd. Cépaduès, 2009, vol. 9, num. 1.
      https://hal.science/hal-00476126
    • [19] H. Nguyen, G. Ramstein, P. Leray, Y. Jacques. Reconstruction of gene regulation networks from microarray data by Bayesian networks. In MODGRAPH - Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM). 09-06-2009
      https://nantes-universite.hal.science/hal-00582682
    • [20] P. Leray, P. Gallinari. De l'utilisation d'OBD pour la sélection de variables dans les Perceptrons Multi-couches. In Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle ; éd. Lavoisier, 2001, vol. 15, num. 3-4.
      https://hal.science/hal-01176949

Conférences internationales avec comité de lecture et actes (COMM_INT)

    • [21] J. Blanchard, P. Leray, F. Marandel, T. Piton. Une extension des modèles graphiques de durée pour estimer l'évolution des coûts de maintenance dans le logement résidentiel. In 11èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes, juin 2023, Nantes, France.
      https://hal.science/hal-04154066
    • [22] Q. Couland, P. Leray, A. Boulahmel. Un modèle générique avec structuration des compétences et facteurs externes pour le Bayesian Knowledge Tracing. In 11èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes, juin 2023, Nantes,, France.
      https://hal.science/hal-04154064
    • [23] M. Roche, H. Pentecouteau, P. Leray, F. Djelil, E. Bertrand, J. Eneau, J. Gilliot, G. Lameul. L’autorégulation des apprentissages dans une formation pour adulte. L’exemple de la demande d’aide. In Congrès international d’Actualité de la Recherche en Éducation et en Formation (AREF), septembre 2022, Lausanne, Suisse.
      https://hal.science/hal-03794749
    • [24] M. Monvoisin, P. Leray, M. Ritou. Unsupervised co-training of bayesian networks for condition prediction. In 34th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, 2021, Kuala Lumpur, Malaisie.
      https://hal.science/hal-03172024
    • [25] M. Monvoisin, P. Leray, M. Ritou. Unsupervised condition monitoring with bayesian networks: an application on high speed machining. In 31th European Safety and Reliability Conference, ESREL 2021, 2021, Angers, France.
      https://hal.science/hal-03324339
    • [26] F. Djelil, J. Gilliot, S. Garlatti, P. Leray. Supporting Self-Regulation Learning Using a Bayesian Approach. Some Preliminary Insights. In International Joint Conference on Artificial Intelligence IJCAI-21, Workshop Artificial Intelligence for Education, août 2021, Montreal (virtual), Canada.
      https://imt-atlantique.hal.science/hal-03325733
    • [27] E. Dufraisse, P. Leray, R. Nedellec, T. Benkhelif. Interactive anomaly detection in mixed tabular data using Bayesian networks. In 10th International Conference on Probabilistic Graphical Models (PGM 2020), septembre 2020, Aalborg, Danemark.
      https://hal.science/hal-03014622
    • [28] D. Antakly, B. Delahaye, P. Leray. Graphical event model learning and verification for security assessment. In 32th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2019), 2019, Graz, Autriche.
      https://hal.science/hal-02129161
    • [29] M. Monvoisin, P. Leray. Multi-task transfer learning for timescale graphical event models. In 15th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2019), 2019, Belgrade, Serbie.
      https://hal.science/hal-02193272
    • [30] S. Rennoij, L. van Der Gaag, P. Leray. On intercausal interactions in probabilistic relational models. In The Eleventh International Symposium on Imprecise Probability: Theories and Applications (ISIPTA ’19), 2019, Ghent, Belgique.
      https://hal.science/hal-02129171
    • [31] T. Kante, P. Leray. A probabilistic relational model approach for fault tree modeling with spatial information and resource management. In 32th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2019), 2019, Graz, Autriche.
      https://hal.science/hal-02129155
    • [32] L. van Der Gaag, P. Leray. Qualitative probabilistic relational models. In The 12th International Conference on Scalable Uncertainty Management (SUM 2018), 2018, Milano, Italie.
      https://hal.science/hal-01891685
    • [33] M. El Abri, P. Leray, N. Essoussi. DAPER joint learning from partially structured Graph Databases. In Third annual International Conference on Digital Economy (ICDEc 2018), 2018, Brest, France.
      https://hal.science/hal-01804057
    • [34] R. Rincé, R. Kervarc, P. Leray. Complex event processing under uncertainty using Markov chains, constraints, and sampling. In 2nd International Joint Conference on Rules and Reasoning (RuleML+RR 2018), 2018, Luxembourg, Luxembourg.
      https://hal.science/hal-01891691
    • [35] R. Chulyadyo, P. Leray. Using Probabilistic Relational Models to Generate Synthetic Spatial or Non-spatial Databases. In Research Challenges in Information Science (RCIS) 2018, 12th International Conference on, mai 2018, Nantes, France.
      https://hal.science/hal-01761901
    • [36] J. Pan, H. Le Capitaine, P. Leray. Relational Constraints for Metric Learning on Relational Data. In Eighth International Workshop on Statistical Relational AI, IJCAI, juillet 2018, Stockholm, Suède.
      https://hal.science/hal-02017253
    • [37] M. El Abri, P. Leray, N. Essoussi. Learning probabilistic relational models with (partially structured) graph databases. In 14th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2017), 2017, Hammamet, Tunisie.
      https://hal.science/hal-01619318
    • [38] M. Haddad, P. Leray, A. Levray, K. Tabia. Learning the parameters of possibilistic networks from data: Empirical comparison. In Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 30), 2017, Marco Island, états-Unis.
      https://hal.science/hal-01532494
    • [39] M. Haddad, P. Leray, N. Amor. Possibilistic MDL: a new possibilistic likelihood based score function for imprecise data. In Fourteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2017), 2017, Lugano, Suisse.
      https://hal.science/hal-01532488
    • [40] R. Rincé, R. Kervarc, P. Leray. On the use of walkSAT based algorithms for MLN inference in some realistic applications. In 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2017), 2017, Arras, France.
      https://hal.science/hal-01532492
    • [41] T. Kante, P. Leray. A probabilistic relational model approach for fault trees modeling. In 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2017), 2017, Arras, France.
      https://hal.science/hal-01532490
    • [42] Y. Benhaddou, P. Leray. Customer relationship management and small data - application of bayesian network elicitation techniques for building a lead scoring model. In 14th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2017), octobre 2017, Hammamet, Tunisie.
      https://hal.science/hal-01619307
    • [43] M. Ben Ishak, P. Leray, N. Amor. A hybrid approach for probabilistic relational models structure learning. In 15th International Symposium on Intelligent Data Analysis (IDA 2016), 2016, Stockholm, Suède.
      https://hal.science/hal-01347798
    • [44] M. Haddad, P. Leray, A. Levray, K. Tabia. Possibilistic networks parameter learning: Preliminary empirical comparison. In 8èmes journées francophones de réseaux bayésiens (JFRB 2016), 2016, Clermont-Ferrand, France.
      https://hal.science/hal-01347810
    • [45] N. Ettouzi, P. Leray, M. Ben Messaoud. An exact approach to learning probabilistic relational model. In 8th International Conference on Probabilistic Graphical Models (PGM 2016), 2016, Lugano, Suisse.
      https://hal.science/hal-01347804
    • [46] A. Coutant, H. Le Capitaine, P. Leray. On the equivalence between regularized nmf and similarity-augmented graph partitioning. In 23th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015), 2015, Bruges, Belgique.
      https://hal.science/hal-01150691
    • [47] G. Ramstein, P. Leray. CPD tree learning using contexts as background knowledge. In 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2015), 2015, Compiègne, France.
      https://hal.science/hal-01150694
    • [48] M. Haddad, P. Leray, N. Ben Amor. Learning possibilistic networks from data: a survey.. In 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, Espagne.
      https://hal.science/hal-01150815
    • [49] N. Ben Amor, M. Haddad, P. Leray. Evaluating product-based possibilistic networks learning algorithms. In 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2015), 2015, Compiègne, France.
      https://hal.science/hal-01150813
    • [50] R. Chulyadyo, P. Leray. Integrating spatial information into probabilistic relational model. In 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015), 2015, Paris, France.
      https://hal.science/hal-01201226
    • [51] A. Coutant, H. Le Capitaine, L. Philippe. On the equivalence between regularized NMF and similarity-augmented graph partitioning. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), avril 2015, Bruges, Belgique.
      https://hal.science/hal-01183562
    • [52] A. Coutant, L. Philippe, H. Le Capitaine. Probabilistic Relational Models with Clustering Uncertainty. In IEEE International Joint Conference on Neural Networks (IJCNN 2015), juillet 2015, Killarney, Irlande.
      https://hal.science/hal-01183563
    • [53] A. Le Dorze, B. Duval, L. Garcia, D. Genest, P. Leray, S. Loiseau. Probabilistic Cognitive Maps Semantics of a Cognitive Map when the Values are Assumed to be Probabilities. In International Conference on Agents and Artificial Intelligence (ICAART), 2014, Angers, France.
      https://hal.science/hal-00957935
    • [54] E. Menou, F. Tancret, P. Leray. New data mining techniques in materials science: Bayesian networks to predict the yield stress of Ni-base superalloys. In TMS2014, 143rd Annual Meeting & Exhibition, 2014, San Diego, états-Unis.
      https://hal.science/hal-01016497
    • [55] P. Leray. Advances in Learning with Bayesian Networks. In 6th International Conference on Agents and Artificial Intelligence (ICAART 2014), mars 2014, Angers, France.
      https://hal.science/hal-00957937
    • [56] A. Coutant, L. Philippe, H. Le Capitaine. Learning Probabilistic Relational Models Using Non-Negative Matrix Factorization. In International Florida Artificial Intelligence Research Society (FlAIRS) Conference, mai 2014, Pensacola Beach, Floride, états-Unis.
      https://hal.science/hal-01183565
    • [57] A. Coutant, P. Leray, H. Le Capitaine. Learning Probabilistic Relational Models using Non-Negative Matrix Factorization. In The 27th International FLAIRS Conference, Uncertain Reasoning Special Track, mai 2014, Pensacola Beach, Florida, états-Unis.
      https://hal.science/hal-00958394
    • [58] R. Chulyadyo, P. Leray. A Personalized Recommender System from Probabilistic Relational Model and Users’ Preferences. In Knowledge-Based and Intelligent Information & Engineering Systems 18th Annual Conference, KES-2014, septembre 2014, Gdynia, Pologne.
      https://hal.science/hal-01084449
    • [59] M. Ben Ishak, L. Philippe, N. Amor. Random generation and population of probabilistic relational models and databases. In 26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2014), novembre 2014, Limassol, Chypre.
      https://hal.science/hal-01084510
    • [60] D. Phan, P. Leray, C. Sinoquet. Modeling genetical data with forests of latent trees for applications in association genetics at a large scale. Which clustering method should be chosen?. In International Conference on Bioinformatics Models, Methods and Algorithms, Bioinformatics2015, novembre 2014, Lisbon, Portugal.
      https://hal.science/hal-01084907
    • [61] A. Coutant, P. Leray, H. Le Capitaine. Learning Probabilistic Relational Models using co-clustering methods. In Structured Learning: Inferring Graphs from Structured and Unstructured Inputs (SLG 2013) ICML Workshop, 2013, Atlanta, états-Unis.
      https://hal.science/hal-00819031
    • [62] A. Yasin, P. Leray. Incremental bayesian network structure learning in high dimensional domains. In International Conference on Modeling, Simulation and Applied Optimization (ICMSAO 2013), 2013, Hammamet, Tunisie.
      https://hal.science/hal-00812175
    • [63] G. Trabelsi, P. Leray, M. Ben Ayed, A. Alimi. Benchmarking dynamic bayesian network structure learning algorithms. In International Conference on Modeling, Simulation and Applied Optimization (ICMSAO 2013), 2013, Hammamet, Tunisie.
      https://hal.science/hal-00812171
    • [64] G. Trabelsi, P. Leray, M. Ben Ayed, A. Alimi. Dynamic MMHC: a local search algorithm for dynamic bayesian network structure learning. In International Symposium on Intelligent Data Analysis, 2013, London, Royaume-Uni.
      https://hal.science/hal-00864162
    • [65] M. Ben Ishak, N. Ben Amor, P. Leray. A RBN-based recommender system architecture. In International Conference on Modeling, Simulation and Applied Optimization (ICMSAO 2013), 2013, Hammamet, Tunisie.
      https://hal.science/hal-00812168
    • [66] M. Ben Messaoud, P. Leray, N. Ben Amor. Active learning of causal bayesian networks using ontologies: a case study. In International Joint Conference on Neural Networks, 2013, Dallas, états-Unis.
      https://hal.science/hal-00864156
    • [67] M. Haddad, N. Ben Amor, P. Leray. Imputation of possibilistic data for structural learning of directed acyclic graphs Genova, Italy.. In International Workshop on Fuzzy Logic and Applications, 2013, Genoa, Italie.
      https://hal.science/hal-00864157
    • [68] A. Jarraya, P. Leray, A. Masmoudi. A new implicit parameter estimation for conditional gaussian bayesian networks. In Uncertainty Modeling in Knowledge Engineering and Decision Making, 2012, Istanbul, Turquie.
      https://hal.science/hal-00691835
    • [69] A. Jarraya, P. Leray, A. Masmoudi. Discrete exponential bayesian networks structure learning for density estimation. In International Conference on Intelligent Computing, 2012, Huangshan, Chine.
      https://hal.science/hal-00691834
    • [70] C. Sinoquet, R. Mourad, P. Leray. Forests of latent tree models for the detection of genetic associations. In International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2012), février 2012, Vilamoura, Portugal.
      https://hal.science/hal-00637500
    • [71] A. Jarraya, P. Leray, A. Masmoudi. Discrete exponential bayesian networks: an extension of bayesian networks to discrete natural exponential families. In ICTAI 2011, 2011, Palm Beach County, états-Unis.
      https://hal.science/hal-00645003
    • [72] A. Yasin, P. Leray. immpc: A local search approach for incremental bayesian network structure learning. In IDA 2011, 2011, Porto, Portugal.
      https://hal.science/hal-00645014
    • [73] F. Schnitzler, S. Ammar, P. Leray, P. Geurts, L. Wehenkel. Efficiently approximating markov tree bagging for high-dimensional density estimation. In ECML-PKDD 2011, 2011, Athens, Grèce.
      https://hal.science/hal-00645009
    • [74] H. Nguyen, P. Leray, G. Ramstein. Multiple hypothesis testing and quasi essential graph for comparing two sets of bayesian networks. In KES 2011, 2011, Kaiserslautern, Allemagne.
      https://hal.science/hal-00645006
    • [75] H. Nguyen, P. Leray, G. Ramstein. Summarizing and visualizing a set of bayesian networks with quasi essential graphs. In ASMDA 2011, 2011, Roma, Italie.
      https://hal.science/hal-00645005
    • [76] M. Ben Ishak, P. Leray, N. Ben Amor. A two-way approach for probabilistic graphical models structure learning and ontology enrichment.. In KEOD 2011, 2011, Paris, France.
      https://hal.science/hal-00644993
    • [77] M. Ben Ishak, P. Leray, N. Ben Amor. Ontology-based generation of object oriented bayesian networks. In BMAW 2011, 2011, Barcelona, Espagne.
      https://hal.science/hal-00644992
    • [78] M. Ben Messaoud, P. Leray, N. Ben Amor. Semcado: a serendipitous causal discovery algorithm for ontology evolution. In ARCOE 2011, 2011, Barcelona, Espagne.
      https://hal.science/hal-00645000
    • [79] S. Ammar, P. Leray. Mixture of markov trees for bayesian network structure learning with small datasets in high dimensional space. In ECSQARU 2011, 2011, Belfast, Irlande.
      https://hal.science/hal-00644991
    • [80] M. Ben Messaoud, P. Leray, N. Ben Amor. SemCaDo: a serendipitous strategy for learning causal bayesian networks using ontologies. In The 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, juin 2011, Belfast, Irlande.
      https://hal.science/hal-00596260
    • [81] M. Messaoud, P. Leray, N. Amor. SemCaDo: a serendipitous causal discovery algorithm for ontology evolution. In The IJCAI-11 Workshop on Automated Reasoning about Context and Ontology Evolution, juillet 2011, Barcelone, Espagne.
      https://hal.science/hal-00596261
    • [82] F. Schnitzler, P. Leray, L. Wehenkel. Towards sub-quadratic learning of probability density models in the form of mixtures of trees. In ESANN 2010, 2010, Bruges, Belgique.
      https://hal.science/hal-00487354
    • [83] K. Tabia, P. Leray, L. Mé. From redundant/irrelevant alert elimination to handling idss' reliability and controlling severe attack prediction/false alarm rate tradeoffs. In 5th Conference on Network and Information Systems Security (SARSSI'10), 2010, Rocquebrune Cap-Martin, France.
      https://hal.science/hal-00870800
    • [84] K. Tabia, P. Leray. Handling idss' reliability in alert correlation: A bayesian network-based model for handling IDS's reliability and controlling prediction/false alarm rate tradeoffs. In International Conference on Security and Cryptography (SECRYPT'10), 2010, Athens, Grèce.
      https://hal.science/hal-00866587
    • [85] S. Ammar, P. Leray, L. Wehenkel. Sub-quadratic markov tree mixture models for probability density estimation. In COMPSTAT 2010, 2010, Paris, France.
      https://hal.science/hal-00487353
    • [86] H. Nguyen, G. Ramstein, L. Philippe, Y. Jacques. Differential study of the cytokine network in the immune system: An evolutionary approach based on the Bayesian networks. In The 2nd Asian Conference on Intelligent Information and Database Systems (ACIIDS), mars 2010, Hue City, Viêt Nam.
      https://hal.science/hal-00656723
    • [87] K. Tabia, P. Leray, L. Mé. From redundant/irrelevant alert elimination to handling IDSes reliability and controlling severe attack prediction/false alarm rate tradeoffs. In 5ème Conférence sur la sécurité des architectures réseaux et systèmes d'information, mai 2010, Menton, France.
      https://centralesupelec.hal.science/hal-00534569
    • [88] K. Tabia, P. Leray. Bayesian network-based approaches for severe attack prediction and handling IDSs' reliability. In International Conference on Information Processing and Management of Uncertainty (IPMU'10), juin 2010, Dortmund, Allemagne.
      https://hal.science/hal-00481056
    • [89] K. Tabia, P. Leray. Handling IDS' reliability in alert correlation: A Bayesian network-based model for handling IDS's reliability and controlling prediction/false alarm rate tradeoffs. In International Conference on Security and Cryptography (SECRYPT'2010), juillet 2010, Athène, Grèce.
      https://hal.science/hal-00481054
    • [90] R. Mourad, C. Sinoquet, P. Leray. Learning Hierarchical Bayesian Networks for Genome-Wide Association Studies. In COMPSTAT, Nineteenth International Conference on Computational Statististics, août 2010, Paris, France.
      https://hal.science/hal-00484696
    • [91] S. Ammar, P. Leray, F. Schnitzler, L. Wehenkel. Sub-quadratic Markov tree mixture learning based on randomizations of the Chow-Liu algorithm. In PGM 2010, septembre 2010, Helsinki, Finlande.
      https://hal.science/hal-00568028
    • [92] M. Ben Messaoud, P. Leray, N. Ben Amor. Integrating ontological knowledge for iterative causal discovery and vizualisation. In ECSQARU 2009, 2009, Verona, Italie.
      https://hal.science/hal-00412286
    • [93] S. Ammar, P. Leray, B. Defourny, L. Wehenkel. Probability density estimation by perturbing and combining tree structured markov networks. In ECSQARU 2009, 2009, Verona, Italie.
      https://hal.science/hal-00412283
    • [94] R. Donat, L. Bouillaut, P. Aknin, P. Leray, S. Bondeux. Specific graphical models for analyzing the reliability. In MED'08, 2008, Ajaccio, France.
      https://hal.science/hal-00412495
    • [95] R. Donat, L. Bouillaut, P. Aknin, P. Leray. Reliability analysis using graphical duration models. In ARES 2008, 2008, Barcelona, Espagne.
      https://hal.science/hal-00412299
    • [96] S. Ammar, P. Leray, B. Defourny, L. Wehenkel. High-dimensional probability density estimation with randomized ensembles of tree structured bayesian networks. In PGM 2008, 2008, Hirtshals, Danemark.
      https://hal.science/hal-00412288
    • [97] L. Bouillaut, O. François, P. Leray, P. Aknin, S. Dubois. Dynamic bayesian networks modelling maintenance strategies: Prevention of broken rails. In 8th World Congress on Railway Research (WCRR 2008), mai 2008, Seoul, Corée du Sud.
      https://hal.science/hal-00412291
    • [98] S. Ammar, P. Leray, B. Defourny, L. Wehenkel. Density estimation with ensembles of randomized poly-trees. In BENELEARN 2008, mai 2008, Spa, Belgique.
      https://hal.science/hal-00568050
    • [99] A. Faour, P. Leray, B. Eter. Growing hierarchical self-organizing map for alarm filtering in network intrusion detection systems. In NTMS'07, 2007, Paris, France.
      https://hal.science/hal-00412943
    • [100] O. François, P. Leray. Generation of incomplete test-data using bayesian networks. In IJCNN, 2007, Orlando, états-Unis.
      https://hal.science/hal-00412939
    • [101] S. Meganck, P. Leray, B. Manderick. Causal graphical models with latent variables: Learning and inference. In ECSQARU, 2007, Hammamet, Tunisie.
      https://hal.science/hal-00412946
    • [102] G. Mallet, P. Leray, H. Polaert, C. Tolant, P. Eudeline. Dynamic Compact Thermal Model with Neural Networks for Radar Applications. In THERMINIC 2006, septembre 2006, Nice, France.
      https://hal.science/hal-00171366
    • [103] P. Leray, P. Gallinari. Data Fusion for Diagnosis in a Telecommunication Network. In ICANN 1998 - 8th International Conference of Artificial Neural Networks, septembre 1998, Skövde, Suède.
      https://hal.science/hal-01617480
    • [104] P. Leray, P. Gallinari, E. Didelet. Neural Networks for Alarm Generation in Telephone Management. In Eighth Workshop on Principles of Diagnostic, 1997, Mont Saint-Michel, France.
      https://hal.science/hal-01649027
    • [105] P. Leray, P. Gallinari, E. Didelet. Local diagnosis for real-time network traffic management. In International Workshop on Applications of Neural Networks to Telecommunications (IWANNT'97), juin 1997, Melbourne, Australie.
      https://hal.science/hal-01624733

Conférences nationales avec comité de lecture et actes (COMM_NAT)

    • [106] W. Fathallah, N. Ben Amor, P. Leray. An optimized Quantum circuit representation of Bayesian networks. In 11èmes Journées Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes, juin 2023, Nantes, France.
      https://hal.science/hal-04154072
    • [107] M. Rifi, P. Leray. État de l’art des méthodes de détections de communautés dans les réseaux bipartis binaires et pondérés.. In 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.
      https://hal.science/hal-01348300
    • [108] T. Gherasim, B. Ed-Dahmouni, P. Leray. Détection et prédiction de défaillances dans un parc d'éoliennes à l'aide de réseaux bayésiens. In 8èmes journées francophones de réseaux bayésiens (JFRB 2016), 2016, Clermont-Ferrand, France.
      https://hal.science/hal-01347808
    • [109] D. Phan, P. Leray, C. Sinoquet. Impact du choix de la méthode de partitionnement pour les forêts d'arbres latents. In SFC2015, septembre 2015, Nantes, France.
      https://hal.science/hal-01205544
    • [110] A. Coutant, P. Leray, H. Le Capitaine. Apprentissage de modèles relationnels probabilistes par factorisation non-négative de matrices. In 7èmes journées francophones sur les réseaux bayésiens (JFRB 2014), 2014, Paris, France.
      https://hal.science/hal-01005777
    • [111] F. Tancret, P. Leray, E. Menou. Bayesian networks in materials science: new tools to predict the properties of materials. In TMS2014 - 143rd Annual Meeting & Exhibition, 2014, San Diego, états-Unis.
      https://hal.science/hal-01016503
    • [112] G. Trabelsi, P. Leray, M. Ben Ayed, A. Alimi. Évaluation des algorithmes d'apprentissage de structure pour les réseaux bayésiens dynamiques.. In 7èmes journées francophones sur les réseaux bayésiens (JFRB 2014), 2014, Paris, France.
      https://hal.science/hal-01005775
    • [113] M. Ben Ishak, P. Leray, N. Ben Amor. La génération aléatoire de réseaux bayésiens relationnels. In 7ème journées francophones sur les réseaux bayésiens (JFRB 2014), 2014, Paris, France.
      https://hal.science/hal-01005772
    • [114] M. Haddad, N. Ben Amor, P. Leray. Apprentissage des réseaux possibilistes à partir de données: un survol. In 7èmes journées francophones sur les réseaux bayésiens (JFRB 2014), 2014, Paris, France.
      https://hal.science/hal-01005780
    • [115] M. Ben Ishak, R. Chulyadyo, A. Abdelwahab, M. Ramirez, P. Leray, N. Ben Amor. Relational bayesian networks for recommender systems: review and comparative study. In ENBIS-SFdS Spring Meeting on graphical causality models: Trees, Bayesian Networks and Big Data, avril 2014, Paris, France.
      https://hal.science/hal-00957940
    • [116] A. Le Dorze, B. Duval, L. Garcia, D. Genest, P. Leray, S. Loiseau. Probabilistic cognitive maps. In Septièmes Journées de l'Intelligence Artificielle Fondamentale (JIAF), 2013, Aix en provence, France.
      https://hal.science/hal-00828271
    • [117] C. Sinoquet, R. Mourad, P. Leray. Modeling of genotype data with forests of latent trees to detect genetic causes of diseases. In Ado2013 (Machine Learning and Omics Data), décembre 2013, Lille, France.
      https://hal.science/hal-00915538
    • [118] F. Schnitzler, S. Ammar, P. Leray, P. Geurts, L. Wehenkel. Approximation efficace de mélanges bootstrap d'arbres de markov pour l'estimation de densité.. In Conférence francophone sur l'Apprentissage Automatique, 2012, Nancy, France.
      https://hal.science/hal-00700464
    • [119] F. Schnitzler, S. Ammar, P. Leray, P. Geurts, L. Wehenkel. Approximation efficace de mélanges bootstrap d'arbres de Markov pour l'estimation de densité. In Conférence Francophone sur l'Apprentissage Automatique - CAp 2012, mai 2012, Nancy, France.In Laurent Bougrain (éds.), . , 2012.
      https://inria.hal.science/hal-00745501
    • [120] A. Yasin, P. Leray. Local Skeleton Discovery for Incremental Bayesian Network Structure Learning. In International Conference on Computer Networks and Information Technology (ICCNIT), juillet 2011, Peshawar, Pakistan.
      https://hal.science/hal-00595152
    • [121] F. Schnitzler, P. Leray, L. Wehenkel. Vers un apprentissage subquadratique pour les mélanges d'arbres. In 5èmes Journées Francophones sur les Réseaux Bayésiens (JFRB2010), mai 2010, Nantes, France.
      https://hal.science/hal-00467066
    • [122] K. Tabia, P. Leray. Approches basées sur les réseaux Bayésiens pour la prédiction d'attaques sévères. In 5èmes Journées Francophones sur les Réseaux Bayésiens (JFRB2010), mai 2010, Nantes, France.
      https://hal.science/hal-00467656
    • [123] M. Ben Messaoud, N. Ben Amor, P. Leray. L'intégration des connaissances ontologiques pour l'apprentissage des réseaux bayesiens causaux. In 5èmes Journées Francophones sur les Réseaux Bayésiens (JFRB2010), mai 2010, Nantes, France.
      https://hal.science/hal-00474395
    • [124] S. Ammar, P. Leray, L. Wehenkel. Mélanges sous-quadratiques d'arbres de Markov pour l'estimation de la densité de probabilité. In 5èmes Journées Francophones sur les Réseaux Bayésiens (JFRB2010), mai 2010, Nantes, France.
      https://hal.science/hal-00474295
    • [125] R. Mourad, C. Sinoquet, P. Leray. Apprentissage de réseaux bayésiens hiérarchiques latents pour les études d'association pangénomiques. In Proc. JFRB 2010, 5th French-speaking meeting on Bayesian networks, Nantes, mai 2010, Nantes, France.
      https://hal.science/hal-00484706
    • [126] K. Tabia, P. Leray, L. Mé. From redundant/irrelevant alert elimination to handling IDSs' reliability and controlling severe attack prediction/false alarm rate tradeoffs. In Fifth Conference on Network and Information Systems Security (SARSSI 2010), mai 2010, Nice, France.
      https://hal.science/hal-00481061
    • [127] R. Mourad, C. Sinoquet, P. Leray. Réseaux bayésiens hiérarchiques avec variables latentes pour la modélisation des dépendances entre SNP: une approche pour les études d'association pangénomiques. In Proc. SFC 2010, XVIIth Join Meeting of the French Society of Classification, France, Saint-Denis de la Réunion, 9-11 june, juin 2010, Saint-Denis de la Réunion, France.
      https://hal.science/hal-00484705
    • [128] R. Mourad, C. Sinoquet, P. Leray. Hierarchical Bayesian networks applied to association genetics. In MODGRAPH 2010 (Modèles graphiques probabilistes pour l'intégration de données hétérogènes et la découverte de modèles causaux en biologie), Journée satellite de JOBIM 2010, septembre 2010, Montpellier, France.
      https://hal.science/hal-00915546
    • [129] M. Ben Messaoud, P. Leray, N. Ben Amor. Integrating ontological knowledge for iterative causal discovery and vizualisation. In Workshop on Machine Learning and Visualization, 2009, Hammamet, Tunisie.
      https://hal.science/hal-00412890
    • [130] S. Ammar, P. Leray, B. Defourny, L. Wehenkel. Probability density estimation by perturbing and combining tree structured markov networks. In CAp 2009, 2009, Hammamet, Tunisie.
      https://hal.science/hal-00412883
    • [131] R. Mourad, C. Sinoquet, P. Leray. A Bayesian network approach to model local dependencies among SNPs. In MODGRAPH 2009 Probabilistic graphical models for integration of complex data and discovery of causal models in biology, satellite meeting of JOBIM 2009, juin 2009, Nantes, France.
      https://hal.science/hal-00470528
    • [132] R. Mourad, C. Sinoquet, P. Leray. Modélisation des dépendances locales entre SNP à l'aide d'un réseau bayésien. In Proc. SFC'09, XVIth Join Meeting of the French Society of Classification, actes des 16èmes rencontres de la Société Francophone de Classification, septembre 2009, Grenoble, France.In Gérard d'Aubigny (éds.), Proc. SFC'09, XVIth Join Meeting of the French Society of Classification, actes des 16èmes rencontres de la Société Francophone de Classification. , 2009.
      https://hal.science/hal-00423461
    • [133] A. Faour, P. Leray, B. Eter. Evolutivité d'une architecture en temps réel de filtrage d'alertes générées par les systèmes de détection d'intrusions sur les réseaux. In RFIA 2008, 2008, Amiens, France.
      https://hal.science/hal-00412885
    • [134] L. Bouillaut, R. Donat, P. Aknin, P. Leray. Approches markovienne et semi-markovienne pour la modélisation de la fiabilité et des actions de maintenance d'un système ferroviaire. In Workshop Surveillance, Sûreté et Sécurité des Grands Systèmes (3SGS'08), 2008, Troyes, France.
      https://hal.science/hal-00412902
    • [135] O. François, L. Bouillaut, P. Aknin, P. Leray, S. Dubois. Approche semi-markovienne pour la modélisation de stratégies de maintenance: application à la prévention de rupture du rail. In MOSIM'2008, 2008, Paris, France.
      https://hal.science/hal-00412887
    • [136] R. Donat, P. Leray, L. Bouillaut, P. Aknin. Réseaux bayésiens dynamiques pour la représentation de modèles de durée en temps discret. In Journées Francophone sur les Réseaux Bayésiens, mai 2008, Lyon, France.
      https://hal.science/hal-00259009
    • [137] S. Ammar, P. Leray, L. Wehenkel. Estimation de densité par ensembles aléatoires de poly-arbres. In Journées Francophone sur les Réseaux Bayésiens, mai 2008, Lyon, France.
      https://hal.science/hal-00259868
    • [138] S. Meganck, P. Leray, B. Manderick. UnCaDo: Unsure Causal Discovery. In Journées Francophone sur les Réseaux Bayésiens, mai 2008, Lyon, France.
      https://hal.science/hal-00259692
    • [139] R. Donat, L. Bouillaut, P. Aknin, P. Leray, D. Levy. A generic approach to model complex system reliability using graphical duration models. In Mathematical Methods in Reliability: Methodology and Practice (MMR 2007),, 2007, Glasgow, Royaume-Uni.
      https://hal.science/hal-00412501
    • [140] P. Leray, H. Zaragoza, F. d'Alché-Buc. Pertinence des mesures de confiance en classification. In Conférence francophone RFIA, février 2000, Paris, France.
      https://hal.science/hal-01573394
    • [141] L. Oisel, F. Fleuret, P. Horain, L. Morin, J. Vezien, F. Preteux, A. Gagalowicz, C. Labit, P. Leray. Analyse de séquences non calibrées pour la reconstruction 3D de scène. In Actes 11ème Congrès AFCET-RFIA (RFIA'98), janvier 1998, Clermont-Ferrand, France.
      https://hal.science/hal-00272421

Ouvrages - Chapitres d‘ouvrages et directions d‘ouvrages (OUV)

    • [142] S. Benferhat, P. Leray, K. Tabia. Belief Graphical Models for Uncertainty representation and reasoning. In A Guided Tour of Artificial Intelligence Research, volume II: AI Algorithms. 2020
      https://hal.science/hal-02049801
    • [143] A. Le Dorze, B. Duval, L. Garcia, D. Genest, P. Leray, S. Loiseau. A Probabilistic Semantics for Cognitive Maps. In Agents and Artificial Intelligence 6th International Conference, ICAART 2014, Angers, France, March 6-8, 2014, Revised Selected Papers. 2015
      https://hal.science/hal-01205961
    • [144] D. Phan, P. Leray, C. Sinoquet. Latent Forests to Model Genetical Data for the Purpose of Multilocus Genome-wide Association Studies. Which clustering should be chosen?. In Communication in Computer and Information Science. 31-03-2015
      https://hal.science/hal-01204956
    • [145] S. Benferhat, P. Leray, K. Tabia. Modèles graphiques pour l'incertitude : inférence et apprentissage. In Panorama de l'Intelligence Artificielle, volume 2: Algorithmes pour l'intelligence artificielle. 2014
      https://hal.science/hal-01020910
    • [146] C. Sinoquet, R. Mourad, P. Leray. Forests of latent tree models to decipher genotype-phenotype associations. In Biomedical Engineering Systems and Technologies, Communication in Computer and Information Science 357. 01-01-2013
      https://hal.science/hal-00915532
    • [147] R. Donat, L. Bouillaut, P. Aknin, P. Leray. A dynamic graphical model to represent complex survival distributions. In Advances in Mathematical Modeling for Reliability. 2008
      https://hal.science/hal-00412259
    • [148] P. Leray, S. Meganck, S. Maes, B. Manderick. Causal graphical models with latent variables : learning and inference. In Innovations in Bayesian Networks: Theory and Applications. 2008
      https://hal.science/hal-00412263
    • [151] S. Maes, S. Meganck, P. Leray. An integral approach to causal inference with latent variables. In Causality and Probability in the Sciences. 2007
      https://hal.science/hal-00412264

Theses et HDR (THESE)

Autres publications (AUTRES)

    • [154] R. Chulyadyo, P. Leray. A Framework for Offline Evaluation of Recommender Systems based on Probabilistic Relational Models. Rapport technique, 2017 ; Laboratoire des Sciences du Numérique de Nantes, Capacités SAS, .
      https://hal.science/hal-01666117
    • [155] M. Haddad, P. Leray, N. Amor. Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy. Rapport technique, 2016 ; Laboratoire d'Informatique de Nantes Atlantique.
      https://hal.science/hal-01344821
    • [156] M. Ben Ishak, R. Chulyadyo, P. Leray. Probabilistic Relational Model Benchmark Generation. Rapport technique, 2016 ; LARODEC Laboratory, ISG, Université de Tunis, Tunisia, DUKe research group, LINA Laboratory UMR 6241, University of Nantes, France, DataForPeople, Nantes, France.
      https://hal.science/hal-01273307
    • [157] S. Benferhat, P. Leray. Editorial: Uncertainty in artificial intelligence and databases - International Journal of Approximate Reasoning, 54(7).
      https://hal.science/hal-00864163
    • [158] R. Chulyadyo, P. Leray. Probabilistic Relational Models for Customer Preference Modelling and Recommendation. Rapport technique, 2013 ; Laboratoire d'Informatique de Nantes Atlantique.
      https://hal.science/hal-00967044
    • [159] G. Trabelsi, P. Leray, M. Ben Ayed, A. Alimi. Benchmarking dynamic Bayesian network structure learning algorithms. Rapport technique, 2012 ; .
      https://hal.science/hal-00771258
    • [162] T. Morisseau, R. Mourad, C. Dina, P. Leray, C. Sinoquet. GWAS-AS: assistance for a thorough evaluation of advanced algorithms dedicated to genome-wide association studies.
      https://hal.science/hal-00915535
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