Publications 2002-2025

• 2025
  • M. Bhan, Y. Choho, J.‑N. Vittaut, N. Chesneau, P. Moreau, M.‑J. Lesot : “Towards Achieving Concept Completeness for Textual Concept Bottleneck Models”, Findings of the Association for Computational Linguistics: EMNLP 2025, Suzhou, China, pp. 2007-2024, (Association for Computational Linguistics), (ISBN: 979-8-89176-335-7) (2025)
  • Th. Pontoizeau, R. Caillière, M.‑J. Lesot, Ch. Marsala, N. Museux, J.‑N. Vittaut : “Interprétabilité de bases de règles floues : évaluation numérique et empirique”, Rencontres francophones sur la Logique Floue et ses Applications, Clermont-Ferrand, France, pp. 57-64, (Cépaduès-Editions), (ISBN: 978-2-38395-224-4) (2025)
  • M. Bhan, J.‑N. Vittaut, N. Achache, V. Legrand, A. Blangero, N. Chesneau, J. Murris, M.‑J. Lesot : “Mitigating Text Toxicity with Counterfactual Generation”, Explainable Artificial Intelligence Third World Conference, xAI 2025, Istanbul, Turkey, July 9–11, 2025, Proceedings, Part IV, vol. 2579, Communications in Computer and Information Science, Istanbul, Türkiye, pp. 135-157, (Springer Nature Switzerland) (2025)
  • Th. Pontoizeau, R. Caillière, M.‑J. Lesot, Ch. Marsala, N. Museux, J.‑N. Vittaut : “Assessing the Interpretability of Fuzzy Rule Bases”, 2025 IEEE International Conference on Fuzzy Systems (FUZZ), Reims, France, pp. 1-6, (IEEE), (ISBN: 979-8-3315-4319-8) (2025)
• 2024
  • M. Bhan, J.‑N. Vittaut, N. Chesneau, M.‑J. Lesot : “Self-AMPLIFY: Improving Small Language Models with Self Post Hoc Explanations”, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, FL, United States, pp. 10974-10991, (Association for Computational Linguistics) (2024)
  • A. Koupaï, J. Benet, Y. Yin, J.‑N. Vittaut, P. Gallinari : “GEPS: Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning”, Advances in Neural Information Processing Systems, Vancouver, Canada (2024)
  • L. Serrano, Th. Wang, E. Naour, J.‑N. Vittaut, P. Gallinari : “AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields”, Advances in Neural Information Processing Systems 37 (NeurIPS 2024), Vancouver, Canada, (Curran Associates, Inc.), (ISBN: 9798331314385) (2024)
• 2023
  • L. Serrano, L. Boudec, A. Koupaï, Th. Wang, Y. Yin, J.‑N. Vittaut, P. Gallinari : “Operator Learning with Neural Fields: Tackling PDEs on General Geometries”, Advances in Neural Information Processing Systems, vol. 36, New Orleans (Louisiana), United States, pp. 70581-70611 (2023)
  • M. Bhan, J.‑N. Vittaut, N. Chesneau, M.‑J. Lesot : “TIGTEC : Token Importance Guided TExt Counterfactuals”, Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science, vol. 14171 (3), Lecture Notes in Computer Science, Turin, Italy, pp. 496–512, (Springer), (ISBN: 978-3-031-43417-4) (2023)
  • M. Bhan, J.‑N. Vittaut, N. Chesneau, M.‑J. Lesot : “Enhancing textual counterfactual explanation intelligibility through Counterfactual Feature Importance”, Proceedings of the 3^rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023), Toronto, Canada, pp. 221-231, (Association for Computational Linguistics) (2023)
  • L. Serrano, J.‑N. Vittaut, P. Gallinari : “Operator Learning on Free-Form Geometries”, ICLR 2023 Workshop on Physics for Machine Learning, Kigali, Rwanda (2023)
• 2022
  • Ch. Marsala, I. Bloch, M.‑J. Lesot, S. Tollari, J.‑N. Vittaut : “Recherches en IA explicable dans l’équipe LFI du LIP6”, Bulletin de l'Association Française pour l'Intelligence Artificielle n°116, pp. 9-13, (AFIA) (2022)
• 2019
  • A. Hufschmitt, J.‑N. Vittaut, N. Jouandeau : “Exploiting Game Decompositions in Monte Carlo Tree Search”, Proceedings of the 16^th Advances in Computer Games Conference (ACG’19), Macao, China (2019)
• 2006
  • J.‑N. Vittaut, P. Gallinari : “Supervised and Semi-supervised Machine Learning Ranking”, Advances in XML Information Retrieval and Evaluation: Fifth Workshop of the INitiative for the Evaluation of XML Retrieval (INEX'06), vol. 4518, Lecture Notes in Computer Science, Dagstuhl, Germany, pp. 213-222, (Springer) (2006)
  • J.‑N. Vittaut, P. Gallinari : “Machine Learning Ranking for Structured Information Retrieval”, European Conference on Information Retrieval (ECIR 2006), vol. 3936, Lecture Notes in Computer Science, London, United Kingdom, pp. 338-349, (Springer) (2006)
  • J.‑N. Vittaut, P. Gallinari : “Apprentissage d’ordonnancements en Recherche d’Information Structurée”, 3^e Conference en Recherche d'Information et Applications (CORIA'06), Lyon, France, pp. 17-28 (2006)
• 2005
  • J.‑N. Vittaut, P. Gallinari : “Machine Learning Ranking and INEX’05”, INEX 2005 - 4^th Workshop of the INitiative for the Evaluation of XML Retrieval, vol. 3977, Lecture Notes in Computer Science, Dagstuhl, Germany, pp. 336-343, (Springer) (2005)
• 2004
  • J.‑N. Vittaut, B. Piwowarski, P. Gallinari : “An Algebra for Structured Queries in Bayesian Networks”, 3^rd Workshop of the INitiative for the Evaluation of XML Retrieval (INEX'04), vol. 3493, Lecture Notes in Computer Science, Dagstuhl, Germany, pp. 100-112, (Springer) (2004)
• 2003
  • L. Denoyer, J.‑N. Vittaut, P. Gallinari, S. Brunessaux, S. Brunessaux : “Structured Multimedia Document Classification”, ACM Document Engeneering, Grenoble, France, pp. 153-160, (ACM) (2003)
• 2002
  • J.‑N. Vittaut, M.‑R. Amini, P. Gallinari : “Learning Classification with Both Labeled and Unlabeled Data”, Machine Learning: ECML 2002, 13^th European Conference on Machine Learning, vol. 2430, Lecture Notes in Computer Science, Helsinki, Finland, pp. 468-479, (Springer) (2002)