Philippe Leray

Orcid: 0000-0002-0207-9280

Affiliations:
  • Ecole Polytechnique de l'université de Nantes, France


According to our database1, Philippe Leray authored at least 94 papers between 1996 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Predicting spotify audio features from Last.fm tags.
Multim. Tools Appl., May, 2024

Mining Discriminative Sequential Patterns of Self-regulated Learners.
Proceedings of the Generative Intelligence and Intelligent Tutoring Systems, 2024

2023
An Optimized Quantum Circuit Representation of Bayesian Networks.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2023

2022
Iterative knowledge discovery for fault detection in manufacturing systems.
Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference KES-2022, 2022

2021
Unsupervised Co-training of Bayesian Networks for Condition Prediction.
Proceedings of the Advances and Trends in Artificial Intelligence. From Theory to Practice, 2021

Multi-task Transfer Learning for Bayesian Network Structures.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2021

2020
Integrating expert's knowledge constraint of time dependent exposures in structure learning for Bayesian networks.
Artif. Intell. Medicine, 2020

Interactive Anomaly Detection in Mixed Tabular Data using Bayesian Networks.
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Belief Graphical Models for Uncertainty Representation and Reasoning.
Proceedings of the A Guided Tour of Artificial Intelligence Research: Volume II: AI Algorithms, 2020

2019
On Intercausal Interactions in Probabilistic Relational Models.
Proceedings of the International Symposium on Imprecise Probabilities: Theories and Applications, 2019

A Probabilistic Relational Model for Risk Assessment and Spatial Resources Management.
Proceedings of the Advances and Trends in Artificial Intelligence. From Theory to Practice, 2019

Graphical Event Model Learning and Verification for Security Assessment.
Proceedings of the Advances and Trends in Artificial Intelligence. From Theory to Practice, 2019

Multi-task Transfer Learning for Timescale Graphical Event Models.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2019

2018
Relational Constraints for Metric Learning on Relational Data.
CoRR, 2018

Qualitative Probabilistic Relational Models.
Proceedings of the Scalable Uncertainty Management - 12th International Conference, 2018

Complex Event Processing Under Uncertainty Using Markov Chains, Constraints, and Sampling.
Proceedings of the Rules and Reasoning - Second International Joint Conference, 2018

Using Probabilistic Relational Models to generate synthetic spatial or non-spatial databases.
Proceedings of the 12th International Conference on Research Challenges in Information Science, 2018

DAPER Joint Learning from Partially Structured Graph Databases.
Proceedings of the Digital Economy. Emerging Technologies and Business Innovation, 2018

2017
On the Use of WalkSAT Based Algorithms for MLN Inference in Some Realistic Applications.
Proceedings of the Advances in Artificial Intelligence: From Theory to Practice, 2017

A Probabilistic Relational Model Approach for Fault Tree Modeling.
Proceedings of the Advances in Artificial Intelligence: From Theory to Practice, 2017

Learning the Parameters of Possibilistic Networks from Data: Empirical Comparison.
Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference, 2017

Possibilistic MDL: A New Possibilistic Likelihood Based Score Function for Imprecise Data.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2017

Customer Relationship Management and Small Data - Application of Bayesian Network Elicitation Techniques for Building a Lead Scoring Model.
Proceedings of the 14th IEEE/ACS International Conference on Computer Systems and Applications, 2017

Learning Probabilistic Relational Models with (Partially Structured) Graph Databases.
Proceedings of the 14th IEEE/ACS International Conference on Computer Systems and Applications, 2017

2016
Probabilistic relational model benchmark generation: Principle and application.
Intell. Data Anal., 2016

Probabilistic Relational Model Benchmark Generation.
CoRR, 2016

Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy.
CoRR, 2016

An Exact Approach to Learning Probabilistic Relational Model.
Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

A Hybrid Approach for Probabilistic Relational Models Structure Learning.
Proceedings of the Advances in Intelligent Data Analysis XV - 15th International Symposium, 2016

2015
Introduction.
Rev. d'Intelligence Artif., 2015

Apprentissage des réseaux possibilistes à partir de données.
Rev. d'Intelligence Artif., 2015

SemCaDo: A serendipitous strategy for causal discovery and ontology evolution.
Knowl. Based Syst., 2015

Probabilistic Relational Models with clustering uncertainty.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Learning possibilistic networks from data: a survey.
Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT-15), 2015

On the equivalence between regularized NMF and similarity-augmented graph partitioning.
Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

CPD Tree Learning Using Contexts as Background Knowledge.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2015

Evaluating Product-Based Possibilistic Networks Learning Algorithms.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2015

Integrating spatial information into probabilistic relational models.
Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, 2015

Latent Forests to Model Genetical Data for the Purpose of Multilocus Genome-Wide Association Studies. Which Clustering Should Be Chosen?
Proceedings of the Biomedical Engineering Systems and Technologies, 2015

Modeling Genetical Data with Forests of Latent Trees for Applications in Association Genetics at a Large Scale - Which Clustering Method should Be Chosen?.
Proceedings of the BIOINFORMATICS 2015, 2015

2014
Discrete exponential Bayesian networks: Definition, learning and application for density estimation.
Neurocomputing, 2014

Implicit parameter estimation for conditional Gaussian Bayesian networks.
Int. J. Comput. Intell. Syst., 2014

A Personalized Recommender System from Probabilistic Relational Model and Users' Preferences.
Proceedings of the 18th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, 2014

Random Generation and Population of Probabilistic Relational Models and Databases.
Proceedings of the 26th IEEE International Conference on Tools with Artificial Intelligence, 2014

Advances in Learning with Bayesian Networks.
Proceedings of the ICAART 2014, 2014

A Probabilistic Semantics for Cognitive Maps.
Proceedings of the Agents and Artificial Intelligence - 6th International Conference, 2014

Probabilistic Cognitive Maps - Semantics of a Cognitive Map when the Values are Assumed to be Probabilities.
Proceedings of the ICAART 2014, 2014

Learning Probabilistic Relational Models Using Non-Negative Matrix Factorization.
Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference, 2014

2013
A Survey on Latent Tree Models and Applications.
J. Artif. Intell. Res., 2013

Editorial: Uncertainty in Artificial Intelligence and Databases.
Int. J. Approx. Reason., 2013

Imputation of Possibilistic Data for Structural Learning of Directed Acyclic Graphs.
Proceedings of the Fuzzy Logic and Applications - 10th International Workshop, 2013

Active learning of causal Bayesian networks using ontologies: A case study.
Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

Dynamic MMHC: A Local Search Algorithm for Dynamic Bayesian Network Structure Learning.
Proceedings of the Advances in Intelligent Data Analysis XII, 2013

2012
Probabilistic graphical models for genetic association studies.
Briefings Bioinform., 2012

Discrete Exponential Bayesian Networks Structure Learning for Density Estimation.
Proceedings of the Emerging Intelligent Computing Technology and Applications, 2012

iMMPC: Une approche locale pour l'apprentissage incrémental de la structure des réseaux bayésiens.
Proceedings of the Extraction et gestion des connaissances (EGC'2012), Actes, janvier 31, 2012

Forests of Latent Tree Models to Decipher Genotype-Phenotype Associations.
Proceedings of the Biomedical Engineering Systems and Technologies, 2012

Forests of Latent Tree Models for the Detection of Genetic Associations.
Proceedings of the BIOINFORMATICS 2012 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms, Vilamoura, Algarve, Portugal, 1, 2012

2011
Alert correlation: Severe attack prediction and controlling false alarm rate tradeoffs.
Intell. Data Anal., 2011

A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies.
BMC Bioinform., 2011

Efficiently Approximating Markov Tree Bagging for High-Dimensional Density Estimation.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

Multiple Hypothesis Testing and Quasi Essential Graph for Comparing Two Sets of Bayesian Networks.
Proceedings of the Knowlege-Based and Intelligent Information and Engineering Systems, 2011

iMMPC: A Local Search Approach for Incremental Bayesian Network Structure Learning.
Proceedings of the Advances in Intelligent Data Analysis X - 10th International Symposium, 2011

Discrete Exponential Bayesian Networks: An Extension of Bayesian Networks to Discrete Natural Exponential Families.
Proceedings of the IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011

A Two-way Approach for Probabilistic Graphical Models Structure Learning and Ontology Enrichment.
Proceedings of the KEOD 2011, 2011

SemCaDo: A Serendipitous Strategy for Learning Causal Bayesian Networks Using Ontologies.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2011

Mixture of Markov Trees for Bayesian Network Structure Learning with Small Datasets in High Dimensional Space.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2011

2010
A dynamic Bayesian network to represent discrete duration models.
Neurocomputing, 2010

Handling IDS' Reliability in Alert Correlation - A Bayesian Network-based Model for Handling IDS's Reliability and Controlling Prediction/False Alarm Rate Tradeoffs.
Proceedings of the SECRYPT 2010, 2010

Bayesian Network-Based Approaches for Severe Attack Prediction and Handling IDSs' Reliability.
Proceedings of the Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications, 2010

Towards sub-quadratic learning of probability density models in the form of mixtures of trees.
Proceedings of the 18th European Symposium on Artificial Neural Networks, 2010

Learning Hierarchical Bayesian Networks for Genome-Wide Association Studies.
Proceedings of the 19th International Conference on Computational Statistics, 2010

2009
Integrating Ontological Knowledge for Iterative Causal Discovery and Visualization.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2009

Probability Density Estimation by Perturbing and Combining Tree Structured Markov Networks.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2009

2008
Causal Graphical Models with Latent Variables: Learning and Inference.
Proceedings of the Innovations in Bayesian Networks: Theory and Applications, 2008

Reliability Analysis using Graphical Duration Models.
Proceedings of the The Third International Conference on Availability, 2008

2007
Éditorial.
Rev. d'Intelligence Artif., 2007

Generation of Incompliete Test-Data usinng Bayesinan Networks.
Proceedings of the International Joint Conference on Neural Networks, 2007

Méthodes statistiques et modèles thermiques compacts.
Proceedings of the Extraction et gestion des connaissances (EGC'2007), 2007

Causal Graphical Models with Latent Variables: Learning and Inference.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2007

2006
Learning Semi-Markovian Causal Models using Experiments.
Proceedings of the Third European Workshop on Probabilistic Graphical Models, 2006

Learning the Tree Augmented Naive Bayes Classifier from incomplete datasets.
Proceedings of the Third European Workshop on Probabilistic Graphical Models, 2006

Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach.
Proceedings of the Modeling Decisions for Artificial Intelligence, 2006

Multi-Agent Causal Models for Dependability Analysis.
Proceedings of the The First International Conference on Availability, 2006

Réseaux bayésiens : Apprentissage et diagnostic de systemes complexes.
, 2006

2005
Distributed learning of Multi-Agent Causal Models.
Proceedings of the 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, 2005

Apprentissage de structure des réseaux bayésiens et données incomplètes.
Proceedings of the Extraction et gestion des connaissances (EGC'2005), 2005

Réseaux bayésiens pour le filtrage d'alarmes dans les systèmes de détection d'intrusions.
Proceedings of the Extraction des connaissances : Etat et perspectives (Ateliers de la conférence EGC'2005), 2005

A Learning Algorithm for Multi-Agent Causal Models.
Proceedings of the EUMAS 2005, 2005

2004
Réseaux bayésiens pour la classification Méthodologie et illustration dans le cadre du diagnostic médical.
Rev. d'Intelligence Artif., 2004

Clustering And Bayesian Network Approaches For Discovering Handwriting Strategies Of Primary School Children.
Int. J. Pattern Recognit. Artif. Intell., 2004

Combining classifiers for harmful document filtering.
Proceedings of the Computer-Assisted Information Retrieval (Recherche d'Information et ses Applications), 2004

2001
De l'utilisation d'OBD pour la sélection de variables dans les perceptrons multicouches.
Rev. d'Intelligence Artif., 2001

1996
Diagnosis Tools for Telecommunication Network Traffic Management.
Proceedings of the Artificial Neural Networks, 1996


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