Philippe Leray

发表

Integrating spatial information into probabilistic relational models

Rajani Chulyadyo, Philippe Leray, Philippe Leray, 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Réseaux bayésiens : Apprentissage et diagnostic de systemes complexes

Philippe Leray, Philippe Leray, 2006 .

De l'utilisation d'OBD pour la sélection de variables dans les perceptrons multicouches

Patrick Gallinari, Philippe Leray, 2001, Rev. d'Intelligence Artif..

DAPER Joint Learning from Partially Structured Graph Databases

Nadia Essoussi, Philippe Leray, Marwa El Abri, 2018, ICDEc.

A Survey on Latent Tree Models and Applications

Tengfei Liu, Philippe Leray, Nevin Lianwen Zhang, 2013, J. Artif. Intell. Res..

SemCaDo: A serendipitous strategy for causal discovery and ontology evolution

Nahla Ben Amor, Philippe Leray, Montassar Ben Messaoud, 2015, Knowl. Based Syst..

Generation of incomplete test-data using bayesian networks

Philippe Leray, Olivier Francois, O. François, 2006 .

Active learning of causal Bayesian networks using ontologies: A case study

Nahla Ben Amor, Philippe Leray, Montassar Ben Messaoud, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Diagnosis Tools for Telecommunication Network Traffic Management

Patrick Gallinari, Philippe Leray, Elisabeth Didelet, 1996, ICANN.

A Personalized Recommender System from Probabilistic Relational Model and Users' Preferences

Rajani Chulyadyo, Philippe Leray, Philippe Leray, 2014, KES.

Distributed learning of multi-agent causal models

Bernard Manderick, Philippe Leray, Sam Maes, 2005, IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach

Bernard Manderick, Philippe Leray, Stijn Meganck, 2006, MDAI.

Learning Probabilistic Relational Models Using Non-Negative Matrix Factorization

Hoel Le Capitaine, Philippe Leray, Anthony Coutant, 2014, FLAIRS.

Hierarchical Bayesian networks applied to association genetics

Philippe Leray, Christine Sinoquet, Raphaël Mourad, 2010 .

Semcado: a serendipitous causal discovery algorithm for ontology evolution

Nahla Ben Amor, Philippe Leray, Montassar Ben Messaoud, 2011, IJCAI 2011.

FEATURE SELECTION WITH NEURAL NETWORKS

Patrick Gallinari, Philippe Leray, P. Gallinari, 1999 .

Benchmarking dynamic Bayesian network structure learning algorithms

Adel M. Alimi, Ghada Trabelsi, Philippe Leray, 2013, 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO).

Neural Network Tools for Alarm Generation in the Telephone Network Traffic Management

Patrick Gallinari, Philippe Leray, Elisabeth Didelet, 1997 .

Probabilistic Cognitive Maps - Semantics of a Cognitive Map when the Values are Assumed to be Probabilities

Béatrice Duval, David Genest, Stéphane Loiseau, 2014, ICAART.

Forests of Latent Tree Models for the Detection of Genetic Associations

Philippe Leray, Christine Sinoquet, Raphaël Mourad, 2012, BIOINFORMATICS.

Generation of Incompliete Test-Data usinng Bayesinan Networks

Olivier François, Philippe Leray, O. François, 2007, 2007 International Joint Conference on Neural Networks.

Relational Constraints for Metric Learning on Relational Data pdf

Hoel Le Capitaine, Philippe Leray, Jiajun Pan, 2018, IJCAI 2018.

Handling IDS' reliability in alert correlation: A Bayesian network-based model for handling IDS's reliability and controlling prediction/false alarm rate tradeoffs

Karim Tabia, Philippe Leray, Philippe Leray, 2010, 2010 International Conference on Security and Cryptography (SECRYPT).

A Bayesian network approach to model local dependencies among SNPs

Philippe Leray, Christine Sinoquet, Raphaël Mourad, 2009 .

Local Skeleton Discovery for Incremental Bayesian Network Structure Learning

Philippe Leray, Amanullah Yasin, 2011 .

Learning possibilistic networks from data: a survey

Nahla Ben Amor, Philippe Leray, Maroua Haddad, 2015, IFSA-EUSFLAT.

Learning Semi-Markovian Causal Models using Experiments

Bernard Manderick, Philippe Leray, Sam Maes, 2006, Probabilistic Graphical Models.

A dynamic Bayesian network to represent discrete duration models

Patrice Aknin, Roland Donat, Philippe Leray, 2010, Neurocomputing.

Multiple Hypothesis Testing and Quasi Essential Graph for Comparing Two Sets of Bayesian Networks

Gérard Ramstein, Philippe Leray, Hoai-Tuong Nguyen, 2011, KES.

An Exact Approach to Learning Probabilistic Relational Model

Philippe Leray, Montassar Ben Messaoud, Nourhene Ettouzi, 2016, Probabilistic Graphical Models.

SemCaDo: A Serendipitous Strategy for Learning Causal Bayesian Networks Using Ontologies

Nahla Ben Amor, Philippe Leray, Montassar Ben Messaoud, 2011, ECSQARU.

Dynamic MMHC: A Local Search Algorithm for Dynamic Bayesian Network Structure Learning

Adel M. Alimi, Mounir Ben Ayed, Ghada Trabelsi, 2013, IDA.

Discrete exponential Bayesian networks: Definition, learning and application for density estimation

Afif Masmoudi, Philippe Leray, Aida Jarraya, 2014, Neurocomputing.

Forests of Latent Tree Models to Decipher Genotype-Phenotype Associations

Philippe Leray, Christine Sinoquet, Raphaël Mourad, 2012, BIOSTEC.

Efficiently Approximating Markov Tree Bagging for High-Dimensional Density Estimation

Pierre Geurts, Louis Wehenkel, Philippe Leray, 2011, ECML/PKDD.

Local diagnosis for real-time network traffic management

Patrick Gallinari, Philippe Leray, Elisabeth Didelet, 1997 .

Probabilistic Relational Models with clustering uncertainty

Hoel Le Capitaine, Philippe Leray, Anthony Coutant, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Possibilistic MDL: A New Possibilistic Likelihood Based Score Function for Imprecise Data

Nahla Ben Amor, Philippe Leray, Maroua Haddad, 2017, ECSQARU.

Combining classifiers for harmful document filtering

Bruno Grilhères, Philippe Leray, Stephan Brunessaux, 2004, RIAO.

Using Probabilistic Relational Models to generate synthetic spatial or non-spatial databases

Rajani Chulyadyo, Philippe Leray, Philippe Leray, 2018, 2018 12th International Conference on Research Challenges in Information Science (RCIS).

Learning Hierarchical Bayesian Networks for Genome-Wide Association Studies

Philippe Leray, Christine Sinoquet, Raphaël Mourad, 2010, COMPSTAT.

On the equivalence between regularized NMF and similarity-augmented graph partitioning

Hoel Le Capitaine, Philippe Leray, Anthony Coutant, 2015, ESANN.

Alert correlation: Severe attack prediction and controlling false alarm rate tradeoffs

Karim Tabia, Philippe Leray, Philippe Leray, 2011, Intell. Data Anal..

Discrete Exponential Bayesian Networks Structure Learning for Density Estimation

Afif Masmoudi, Philippe Leray, Aida Jarraya, 2012, ICIC.

A Probabilistic Relational Model Approach for Fault Tree Modeling

Philippe Leray, Thierno Kanté, 2017, IEA/AIE.

Réseaux bayésiens pour la classification Méthodologie et illustration dans le cadre du diagnostic médical

Olivier François, Philippe Leray, Philippe Leray, 2004, Rev. d'Intelligence Artif..

Incremental Bayesian network structure learning in high dimensional domains

Philippe Leray, Amanullah Yasin, Philippe Leray, 2013 .

Approches basées sur les réseaux Bayésiens pour la prédiction d'attaques sévères

Karim Tabia, Philippe Leray, Philippe Leray, 2010 .

Complex Event Processing Under Uncertainty Using Markov Chains, Constraints, and Sampling

Romain Kervarc, Philippe Leray, Romain Rincé, 2018, RuleML+RR.

Probability Density Estimation by Perturbing and Combining Tree Structured Markov Networks

Louis Wehenkel, Boris Defourny, Philippe Leray, 2009, ECSQARU.

Editorial: Uncertainty in Artificial Intelligence and Databases

Salem Benferhat, Philippe Leray, 2013, Int. J. Approx. Reason..

Editorial

Philippe Leray, J. Luo, 2011, The Visual Computer.

Impact of stochastic process variations on overlay mark fidelity "towards the 5nm node"

Chris A. Mack, Dana Klein, Roel Gronheid, 2017, Advanced Lithography.

Modeling Genetical Data with Forests of Latent Trees for Applications in Association Genetics at a Large Scale - Which Clustering Method should Be Chosen?

Philippe Leray, Christine Sinoquet, Duc-Thanh Phan, 2015, BIOINFORMATICS.

On the Use of WalkSAT Based Algorithms for MLN Inference in Some Realistic Applications

Romain Kervarc, Philippe Leray, Romain Rincé, 2017, IEA/AIE.

A Probabilistic Relational Model for Risk Assessment and Spatial Resources Management

Philippe Leray, Thierno Kanté, 2019, IEA/AIE.

iMMPC: Une approche locale pour l'apprentissage incrémental de la structure des réseaux bayésiens

Philippe Leray, Amanullah Yasin, 2012, EGC.

Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy pdf

Nahla Ben Amor, Philippe Leray, Maroua Haddad, 2016, ArXiv.

Integrating Ontological Knowledge for Iterative Causal Discovery and Visualization

Nahla Ben Amor, Philippe Leray, Montassar Ben Messaoud, 2009, ECSQARU.

Réseaux bayésiens pour le filtrage d'alarmes dans les systèmes de détection d'intrusions

Ahmad Faour, Philippe Leray, Cédric Foll, 2005, EGC.

Advances in Learning with Bayesian Networks

Philippe Leray, 2014, ICAART.

Qualitative Probabilistic Relational Models

Linda C. van der Gaag, Philippe Leray, L. V. D. Gaag, 2018, SUM.

Forests of hierarchical latent models for association genetics

Philippe Leray, Christine Sinoquet, Raphaël Mourad, 2010 .

A Probabilistic Semantics for Cognitive Maps

Béatrice Duval, David Genest, Stéphane Loiseau, 2014, ICAART.

CPD Tree Learning Using Contexts as Background Knowledge

Gérard Ramstein, Philippe Leray, 2015, ECSQARU.

Approches markovienne et semi-markovienne pour la modélisation de la fiabilité et des actions de maintenance d'un système ferroviaire

Patrice Aknin, Roland Donat, Philippe Leray, 2008 .

Causal Graphical Models with Latent Variables: Learning and Inference

Bernard Manderick, Philippe Leray, Sam Maes, 2008, Innovations in Bayesian Networks.

Clustering And Bayesian Network Approaches For Discovering Handwriting Strategies Of Primary School Children

Jacques Labiche, Laurent Heutte, Philippe Leray, 2004, Int. J. Pattern Recognit. Artif. Intell..

Méthodes statistiques et modèles thermiques compacts

Philippe Leray, Grégory Mallet, Hubert Polaert, 2007, EGC.

Causal inference in graphical models with latent variables. From theory to practice.

Philippe Leray, Sam Maes, Stijn Meganck, 2006 .

Multi-agent causal models for dependability analysis

Philippe Leray, Sam Maes, S. Maes, 2006, First International Conference on Availability, Reliability and Security (ARES'06).

Learning the Tree Augmented Naive Bayes Classifier from incomplete datasets

Olivier François, Philippe Leray, 2006, Probabilistic Graphical Models.

A Learning Algorithm for Multi-Agent Causal Models

Bernard Manderick, Philippe Leray, Sam Maes, 2005, EUMAS.

A RBN-based recommender system architecture

Philippe Leray, Mouna Ben Ishak, Nahla Ben Amor, 2013, 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO).

Introduction

Philippe Leray, Grégory Nuel, 2010, Rev. d'Intelligence Artif..

On Intercausal Interactions in Probabilistic Relational Models

Silja Renooij, Linda C. van der Gaag, Philippe Leray, 2019, ISIPTA.

Bayesian Network-Based Approaches for Severe Attack Prediction and Handling IDSs' Reliability

Karim Tabia, Philippe Leray, Philippe Leray, 2010, IPMU.

Towards sub-quadratic learning of probability density models in the form of mixtures of trees

Louis Wehenkel, Philippe Leray, François Schnitzler, 2010, ESANN.

Evolutivité d'une architecture en temps réel de filtrage d'alertes générées par les systèmes de détection d'intrusions sur les réseaux

Ahmad Faour, B. Eter, Philippe Leray, 2008 .

Imputation of Possibilistic Data for Structural Learning of Directed Acyclic Graphs

Nahla Ben Amor, Philippe Leray, Maroua Haddad, 2013, WILF.

Estimation de densité par ensembles aléatoires de poly-arbres

Louis Wehenkel, Philippe Leray, Sourour Ammar, 2008 .

Apprentissage des réseaux possibilistes à partir de données

Nahla Ben Amor, Philippe Leray, Maroua Haddad, 2015, Rev. d'Intelligence Artif..

A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies

Philippe Leray, Christine Sinoquet, Raphaël Mourad, 2011, BMC Bioinformatics.

Customer Relationship Management and Small Data — Application of Bayesian Network Elicitation Techniques for Building a Lead Scoring Model

Philippe Leray, Youssef Benhaddou, Philippe Leray, 2017, 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA).

Causal Graphical Models with Latent Variables: Learning and Inference

Bernard Manderick, Philippe Leray, Stijn Meganck, 2007, ECSQARU.

Discrete Exponential Bayesian Networks: An Extension of Bayesian Networks to Discrete Natural Exponential Families

Afif Masmoudi, Philippe Leray, Aida Jarraya, 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

Apprentissage de structure des réseaux bayésiens et données incomplètes

Olivier François, Philippe Leray, 2005, EGC.

Learning a forest of Hierarchical Bayesian Networks to model dependencies between genetic markers

Philippe Leray, Christine Sinoquet, Raphaël Mourad, 2010 .

Implicit parameter estimation for conditional Gaussian Bayesian networks

Afif Masmoudi, Philippe Leray, Aida Jarraya, 2014, Int. J. Comput. Intell. Syst..

Learning Probabilistic Relational Models with (Partially Structured) Graph Databases

Nadia Essoussi, Philippe Leray, Marwa El Abri, 2017, 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA).

Evaluating Product-Based Possibilistic Networks Learning Algorithms

Nahla Ben Amor, Philippe Leray, Maroua Haddad, 2015, ECSQARU.

Mixture of Markov Trees for Bayesian Network Structure Learning with Small Datasets in High Dimensional Space

Philippe Leray, Sourour Ammar, Philippe Leray, 2011, ECSQARU.

Reliability Analysis using Graphical Duration Models

Patrice Aknin, Roland Donat, Philippe Leray, 2008, 2008 Third International Conference on Availability, Reliability and Security.

Probabilistic Relational Model Benchmark Generation pdf

Rajani Chulyadyo, Philippe Leray, Mouna Ben Ishak, 2016, ArXiv.

Latent Forests to Model Genetical Data for the Purpose of Multilocus Genome-Wide Association Studies. Which Clustering Should Be Chosen?

Philippe Leray, Christine Sinoquet, Duc-Thanh Phan, 2015, BIOSTEC.

Probabilistic graphical models for genetic association studies

Philippe Leray, Christine Sinoquet, Raphaël Mourad, 2012, Briefings Bioinform..

Learning the Parameters of Possibilistic Networks from Data: Empirical Comparison

Karim Tabia, Philippe Leray, Amélie Levray, 2017, FLAIRS Conference.

A Hybrid Approach for Probabilistic Relational Models Structure Learning

Nahla Ben Amor, Philippe Leray, Mouna Ben Ishak, 2016, IDA.

iMMPC: A Local Search Approach for Incremental Bayesian Network Structure Learning

Philippe Leray, Amanullah Yasin, 2011, IDA.

Graphical Event Model Learning and Verification for Security Assessment

Benoît Delahaye, Philippe Leray, Dimitri Antakly, 2019, IEA/AIE.

A generic approach to model complex system reliability using graphical duration models

Roland Donat, Philippe Leray, Laurent Bouillaut, 2007 .

Approche semi-markovienne pour la modélisation de stratégies de maintenance: application à la prévention de rupture du rail

Patrice Aknin, Philippe Leray, Stéphane Dubois, 2008 .

A dynamic graphical model to represent complex survival distributions

Roland Donat, Philippe Leray, Laurent Bouillaut, 2008 .

Probabilistic relational model benchmark generation: Principle and application

Nahla Ben Amor, Philippe Leray, Mouna Ben Ishak, 2016, Intell. Data Anal..

R?seaux bay?siens dynamiques pour la repr?sentation de mod?les de dur?e en temps discret

Patrice Aknin, Roland Donat, Philippe Leray, 2008 .

A Two-way Approach for Probabilistic Graphical Models Structure Learning and Ontology Enrichment

Nahla Ben Amor, Philippe Leray, Mouna Ben Ishak, 2011, KEOD.

Multi-task Transfer Learning for Bayesian Network Structures

Philippe Leray, Guillaume Raschia, Montassar Ben Messaoud, 2021, ECSQARU.

Unsupervised Co-training of Bayesian Networks for Condition Prediction

Philippe Leray, Mathieu Ritou, Mathilde Monvoisin, 2021, IEA/AIE.

Incremental discovery of new defects: application to screwing process monitoring

Philippe Leray, Mathieu Ritou, Mahmoud Ferhat, 2021 .

Sub-quadratic markov tree mixture models for probability density estimation

Louis Wehenkel, Philippe Leray, Sourour Ammar, 2010 .

Une modélisation de haut niveau des stratégies d'écriture motrice d'enfants en scolarité primaire : approche bayésienne

Philippe Leray, Iyad Zaarour, Daniel Mellier, 2005 .

Étude Comparative d’Algorithmes d’Apprentissage de Structure dans les Réseaux Bayésiens

Philippe Leray, Olivier Francois, Philippe Leray, 2004 .

Visualization of Pairwise and Multilocus Linkage Disequilibrium Structure Using Latent Forests

Philippe Leray, Christine Sinoquet, Christian Dina, 2011, PloS one.

Pertinence des mesures de confiance en classification

Philippe Leray, Florence d'Alché-Buc, Hugo Zaragoza, 2000 .

Apprentissage et diagnostic de systemes complexes : reseaux de neurones et reseaux bayesiens. application a la gestion en temps reel du trafic telephonique francais

Philippe Leray, 1998 .