Maître de Conférences Équipe : DECISION Sorbonne Université - LIP6 Boîte courrier 169 Couloir 26-00, Étage 4, Bureau 403 4 place Jussieu 75252 PARIS CEDEX 05 Tel: 01 44 27 71 48 https://webia.lip6.fr/~phw/
Activité de recherche
Amélioration des méthodes de calcul d'inférences exactes et approchées dans un réseau bayésien
Apprentissage de la structure et des paramètres
Cadre orienté objet de modélisation de réseaux bayésiens (OOBN, MEBN, PRM) et algorithmes associés;
Diagnostic à base de réseaux bayésiens
Apprentissage par renforcement dans les (PO)MDPs
Neuf docteurs (2012 - 2024) à Sorbonne Université
2024
HADJ ALI Mahdi : Causalité et explication des modèles prédictifs.
CHARON Clara : Classification probabiliste pour la prédiction et l’explication d’événements de santé défavorables et évitables en EHPAD.
2022
LASSERRE Marvin : Apprentissages dans les Réseaux Bayésiens à Base de Copules Non-Paramétriques.
2021
DUCAMP Gaspard : Optimisation de la compilation de règles métier probabilistes à l’aide de PRM.
2017
AGLI Hamza : Raisonnement incertain pour les règles métiers.
2016
MAGNAN Jean-Christophe : Représentations Graphiques de Fonctions et Processus Décisionnels Markoviens Factorisés.
2015
CHEN Yang : Apports à la Modélisation de l'Elève pour l'Apprentissage Individualisé.
MAESANO Ariele-Paolo : Scheduling Bayesien dynamique pour le test des composition de services.
2012
TORTI Lionel : Inférence probabiliste structurée dans les modèles graphiques probabilistes orientés-objet.
Publications 1998-2024
2024
M. HADJ ALI, Y. Le Biannic, P.‑H. Wuillemin : “Quantifying a Causal Effect from a CPDAG with Targeted Exogenous Causal Knowledge”, ECAI 2024 - 27th European Conference on Artificial Intelligence, vol. 392, Frontiers in Artificial Intelligence and Applications, Santiago de compostela, Galicia, Spain, pp. 3187-3194, (IOS Press) (2024)
B. Chassagnol, A. Bichat, Ch. Boudjeniba, P.‑H. Wuillemin, M. Guedj, D. Gohel, G. Nuel, E. Becht : “Gaussian Mixture Models in R”, The R Journal, vol. 15 (2), pp. 56-76, (R Foundation for Statistical Computing) (2023)
Ch. Dang, C. Bazgan, T. Cazenave, M. Chopin, P.‑H. Wuillemin : “Warm-Starting Nested Rollout Policy Adaptation with Optimal Stopping”, 37th AAAI Conference on Artificial Intelligence, vol. 37 (10), Proceedings of the AAAI Conference on Artificial Intelligence, Washington, D.C., United States, pp. 12381-12389 (2023)
C. Charon, P.‑H. Wuillemin, J. Belmin : “Improving Pressure Ulcers Prediction in Nursing Homes with ML Algorithm”, Caring is Sharing – Exploiting the Value in Data for Health and Innovation, vol. 302, Studies in Health Technology and Informatics, Göteborg, Sweden, pp. 350-351, (IOS Press) (2023)
Ch. Dang, C. Bazgan, T. Cazenave, M. Chopin, P.‑H. Wuillemin : “Monte Carlo Search Algorithms for Network Traffic Engineering”, 23e congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, Villeurbanne - Lyon, France (2022)
M. HADJ ALI, Y. Le Biannic, P.‑H. Wuillemin : “Réseaux bayésiens et valeurs de Shapley”, 10es Journée Francophones sur les Réseaux Bayésiens et les Modèles Graphiques Probabilistes (JFRB 2021), Porquerolles, France (2021)
Ch. Dang, C. Bazgan, T. Cazenave, M. Chopin, P.‑H. Wuillemin : “Monte Carlo Search Algorithms for Network Traffic Engineering”, Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings,, vol. 12978, Lecture Notes in Computer Science, Bilbao, Spain, pp. 486-501, (Springer) (2021)
G. Ducamp, Ph. Bonnard, Ch. De Sainte Marie, P.‑H. Wuillemin : “Advanced Syntax and Compilation for Probabilistic Production Rules with PRM”, 14th International Rule Challenge, 4th Doctoral Consortium, and 6th Industry Track, vol. 2644, Proceedings of the 14th International Rule Challenge, 4th Doctoral Consortium, and 6th Industry Track, Oslo, Norway, pp. 103-110 (2020)
G. Ducamp, Ph. Bonnard, P.‑H. Wuillemin : “Uncertain Reasoning in Rule-Based Systems Using PRM”, FLAIRS 33 - 33rd Florida Artificial Intelligence Research Society Conference, Miami, United States, pp. 617-620, (AAAI) (2020)
2019
M. Munch, J. Dibie‑Barthelemy, P.‑H. Wuillemin, C. Manfredotti : “Interactive Causal Discovery in Knowledge Graphs”, PROFILES/SEMEX@ISWC 2019, vol. 2465, CEUR Workshop Proceedings, Auckland, New Zealand, pp. 78-93, (CEUR-WS.org) (2019)
G. Ducamp, Ph. Bonnard, Ch. De Sainte Marie, Ch. Gonzales, P.‑H. Wuillemin : “Improving Probabilistic Rules Compilation using PRM”, RuleML+RR Doctoral Consortium 2018 (2nd International Joint Conference on Rules and Reasoning ), Esch-sur-Alzette, Luxembourg (2018)
M. Ruiz Cuevas, N. Sokolovska, P.‑H. Wuillemin, J.‑D. Zucker : “Detecting Low-Complexity Confounders from Data”, ICML / IJCAI / AAMAS FAIM'18 Workshop on CausalML, Stockholm, Sweden (2018)
M. Han, P.‑H. Wuillemin, P. Senellart : “Focused Crawling through Reinforcement Learning”, 18th International Conference on Web Engineering (ICWE 2018), vol. 10845, Lecture Notes in Computer Science, Cáceres, Spain, pp. 261-278, (Springer) (2018)
V. Delcroix, P.‑H. Wuillemin : “RÉSEAUX BAYÉSIENS ET MODÈLES PROBABILISTES”, Revue des Sciences et Technologies de l'Information - Série RIA : Revue d'Intelligence Artificielle, vol. 32 (1), France, (Lavoisier) (2018)
Ch. Gonzales, L. Torti, P.‑H. Wuillemin : “aGrUM: a Graphical Universal Model framework”, IEA/AIE 2017 - 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, vol. 10351, Lecture Notes in Computer Science, Arras, France, pp. 171-177, (Springer) (2017)
M. Hourbracq, P.‑H. Wuillemin, Ch. Gonzales, . Baumard : “Real time learning of non-stationary processes with dynamic Bayesian Networks”, Information Processing and Management of Uncertainty in Knowledge-Based Systems, vol. 610, Communications in Computer and Information Science, Eindhoven, Netherlands, pp. 338-350, (Springer International Publishing) (2016)
J.‑Ch. Magnan, P.‑H. Wuillemin : “On-line Learning of Multi-valued Decision Diagrams”, Proceedings of the Twenty-Eighth International Florida Artificial Intelligence Research Society Conference, Hollywood, Florida, United States, pp. 576-580 (2015)
S. Herbold, J. Grabowski, P. Harms, L. Hillah, F. Kordon, A.‑P. Maesano, L. Maesano, C. Di Napoli, F. De Rosa, M. Schneider, N. Tonelloto, M.‑F. Wendland, P.‑H. Wuillemin : “The MIDAS Cloud Platform for Testing SOA Applications”, 8th International IEEE Conference on Software Testing, Verification and Validation (ICST), Graz, Austria, pp. 1-8, (IEEE Press) (2015)
H. Agli, Ph. Bonnard, Ch. Gonzales, P.‑H. Wuillemin : “Uncertain Reasoning for Business Rules”, RuleML doctoral consortium, vol. 1211, CEUR Workshop Proceedings, Prague, Czechia, (CEUR-WS.org) (2014)
Y. Chen, P.‑H. Wuillemin, J.‑M. Labat : “Bayesian Student Modeling Improved by Diagnostic Items”, Proceedings of 12th International Conference on Intelligent Tutoring Systems, vol. 8474, Lecture Notes in Computer Science, Honolulu, United States, pp. 144-149, (Springer) (2014)
A. Tonda, E. Lutton, G. Squillero, P.‑H. Wuillemin : “A Memetic Approach to Bayesian Network Structure Learning”, 16th European Conference on Applications of Evolutionary Computation, EvoApplications 2013, vol. 7835, Lecture Notes in Computer Science, Vienna, Austria, pp. 102-111, (Springer), (ISBN: 978-3-642-37191-2) (2013)
O. Barrière, E. Lutton, P.‑H. Wuillemin, C. Baudrit, M. Sicard, N. Perrot : “Cooperative coevolution for agrifood process modeling”, EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation, vol. 447, Studies in Computational Intelligence, Bourglinster, Luxembourg, pp. 247-287, (SPRINGER-VERLAG BERLIN) (2013)
P.‑H. Wuillemin, L. Torti : “Structured Probabilistic Inference”, International Journal of Approximate Reasoning, vol. 53 (7), pp. 946-968, (Elsevier) (2012)
A. Tonda, E. Lutton, R. Reuillon, G. Squillero, P.‑H. Wuillemin : “Bayesian Network Structure Learning from Limited Datasets through Graph Evolution”, EuroGP 2012 - 15th European Conference on Genetic Programming, vol. 7244, Lecture Notes in Computer Science, Malaga, Spain, pp. 254-265, (Springer), (ISBN: 978-3-642-29138-8) (2012)
2011
A.‑P. Maesano, F. De Rosa, L. Maesano, P.‑H. Wuillemin : “Steps towards model-based, inference-driven SOA Testing”, 23rd International Conference on Software & System Engineering and their Applications (ICSSEA '11), Paris - Nov 29 - Dec 1 (2011), Paris, France (2011)
L. Torti, Ch. Gonzales, P.‑H. Wuillemin : “Patterns Discovery for Efficient Structured Probabilistic Inference”, SUM 2011 - 5th International Conference on Scalable Uncertainty Management, vol. 6929, Lecture Notes in Computer Science, Dayton, OH, United States, pp. 247-260, (Springer) (2011)
L. Kong, F. Hajduch, P.‑H. Wuillemin, J. Bastard, S. Fellahi, D. Bonnefont‑Rousselot, R. Bittar, A. Basdevant, J.‑D. Zucker, J. Dore, K. Clement, S. Rizkalla : “Plasma insulin and inflammatory markers prior to weight loss can predict dietary responders”, 47. Annual Meeting of the European Association for the Study of Diabetes (EASD), vol. 54 (S1), Diabetologia, Lisbonne, Portugal, pp. np, (Springer) (2011)
M. Chopin, P.‑H. Wuillemin : “Optimizing the triangulation of Dynamic Bayesian Networks”, Proceedings of the 5th Probabilistic Graphical Models 2010, Helsinki, Finland, pp. 73-80, (Helsinki Institute for Information Technology HIIT) (2010)
L. Torti, P.‑H. Wuillemin : “Structured Value Elimination with D-Separation Analysis”, Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference, Daytona Beach, United States, pp. 122-127 (2010)
C. Baudrit, P.‑H. Wuillemin, M. Sicard, N. Perrot : “A dynamic Bayesian Network to represent a ripening process of a soft mould cheese.”, 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, vol. 5178, Lecture Notes in Computer Science, Zagreb, Croatia, pp. 265-272, (Springer), (ISBN: 978-3-540-85564-4) (2008)
P. Naïm, P.‑H. Wuillemin, Ph. Leray, O. Pourret, A. Becker : “Réseaux bayésiens”, Algorithmes, 424 pages, (Eyrolles) (2007)
2006
Th. Degris, O. Sigaud, P.‑H. Wuillemin : “Chi-square Tests Driven Method for Learning the Structure of Factored MDPs”, Proceedings of the 22nd conference on Uncertainty in Artificial Intelligence, Massachusetts Institute of Technology, Cambridge, MA, United States, pp. 122-129, (AUAI Press) (2006)
Th. Gourdin, O. Sigaud, P.‑H. Wuillemin : “Improving MACS thanks to a comparison with 2TBNs”, GECCO 2004 - Genetic and Evolutionary Computation Conference, vol. 3103, Lecture Notes in Computer Science, Seattle, WA, United States, pp. 810-823, (Springer) (2004)
P. Naïm, P.‑H. Wuillemin, Ph. Leray, O. Pourret, A. Becker : “Réseaux Bayésiens”, Algorithmes, 224 pages, (Eyrolles) (2004)
2001
P.‑H. Wuillemin : “Propagation in Bayesian Networks”, 10th International Symposium on Applied Stochastic Models and Data Analysis, vol. 2, Compiègne, France, pp. 1016-1022 (2001)
Th. Nielsen, P.‑H. Wuillemin, F. Jensen, U. Kjaerulf : “Using ROBDDs for troubleshooting”, 16th conference on Uncertainty in Artificial Intelligence, Stanford, CA, United States, pp. 426-435, (Morgan Kaufmann) (2000)
P.‑H. Wuillemin, O. Bangsø : “Réseaux Probabilistes Orientés Objet”, Langages et modèles à objet, Mont Saint-Hilaire, Québec, Canada, pp. 123-138, (Hermès) (2000)