Publications

Articles de revues internationales

2024 IA explicable Jérémie Bottieau, Gilles Audemard, Steve Bellart, J-M. Lagniez, P. Marquis, Nicolas Szczepanski, Jean-François Toubeau, Logic-based explanations of imbalance price forecasts using boosted trees in Electric Power Systems Research,vol. 235, pp. 110699, 2024.

Articles de conférences internationales

2024 IA explicable Gilles Audemard, Sylvie Coste-Marquis, Pierre Marquis, Mehdi Sabiri, Nicolas Szczepanski, Designing an XAI Interface for Tree-Based ML Models in The 27th European Conference on Artificial Intelligence,2024.

2024 IA explicable Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski, Deriving Provably Correct Explanations for Decision Trees: The Impact of Domain Theories in The 33rd International Joint Conference on Artificial Intelligence,pp. 3688-3696, 2024.

2024 IA explicable Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski, PyXAI: An XAI Library for Tree-Based Models in The 33rd International Joint Conference on Artificial Intelligence,pp. 8601-8605, 2024.

2024 IA explicable Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski, On the Computation of Example-Based Abductive Explanations for Random Forests in The 33rd International Joint Conference on Artificial Intelligence,pp. 3679-3687, 2024.

2023 IA explicable Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski, On Contrastive Explanations for Tree-Based Classifiers in The 26th European Conference on Artificial Intelligence (ECAI'23),IOS Press, 2023.

2023 IA explicable Gilles Audemard, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski, Computing Abductive Explanations for Boosted Trees in 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023),vol. 206, 2023.

2021 Contraintes Nicolas Szczepanski, Gilles Audemard, Laetitia Jourdan, Christophe Lecoutre, Lucien Mousin, Nadarajen Veerapen, A hybrid CP/MOLS approach for multi-objective imbalanced classification in GECCO '21: Genetic and Evolutionary Computation Conference,ACM, pp. 723-731, 2021.

2019 Contraintes Gilles Audemard, Gael Glorian, Jean-Marie Lagniez, Valentin Montmirail, Nicolas Szczepanski, pFactory: A generic library for designing parallel solvers in International Conference on Applied Computing (AC),2019.

2019 Contraintes Gael Glorian, Jean-Marie Lagniez, Valentin Montmirail, Nicolas Szczepanski, An Incremental SAT-Based Approach for Graph Colouring Problem in The 25th International Conference on Principles and Practice of Constraint Programming (CP'19),2019.

2018 Contraintes Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski, DMC: A Distributed Model Counter in 27th International Joint Conference on Artificial Intelligence (IJCAI'18),pp. 1331-1338, 2018.

2017 Gilles Audemard, Jean-Marie Lagniez, Nicolas Szczepanski, Sébastien Tabary, A Distributed Version of Syrup in 20th International Conference on Theory and Applications of Satisfiability Testing (SAT'17),pp. 215-232, 2017.

2016 Gilles Audemard, Jean-Marie Lagniez, Nicolas Szczepanski, Sébastien Tabary, An Adaptive SAT Solver in 22nd International Conference on Principles and Practice of Constraint Programming (CP'16),pp. 30-48, 2016.

Articles de conférences nationales

2023 Gilles Audemard, Steve Bellart, Louenas Bounia, Jean-Marie Lagniez, Pierre Marquis, Nicolas Szczepanski, PyXAI : calculer en Python des explications pour des modèles d'apprentissage supervisé in Extraction et Gestion des Connaissances, EGC,vol. RNTI-E-39, 2023.

2015 Jean-Marie Lagniez, Sébastien Tabary, Nicolas Szczepanski, Swarmsat : un solveur sat massivement parallèle. in 11èmes Journées Francophones de Programmation par Contraintes (JFPC'15),2015.

Rapports techniques

2021 Christophe Lecoutre, Nicolas Szczepanski, PyCSP3: Modeling Combinatorial Constrained Problems in Python 2021.