2002-2024 Publications

• 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)
• 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)