Jean-Noël Vittaut

Jean-Noël Vittaut

Associate Professor of Computer Science

PIF department

PASTIS team

Université Paris 8

I am an associate professor of computer science at Université Paris 8.

Experience

 
 
 
 
 
Université Paris 8
Associate Professor of Computer Science
Université Paris 8
September 2026 – Present Paris, France
 
 
 
 
 
Sorbonne Université
Associate Professor of Computer Science
Sorbonne Université
September 2019 – August 2026 Paris, France
 
 
 
 
 
Université Paris 8 --- Vincennes -- Saint-Denis
Lecturer in Computer Science
Université Paris 8 — Vincennes – Saint-Denis
September 2008 – August 2019 Saint-Denis, France

Responsibilies include:

  • Computer Science Bachelor Degree
  • Training program
 
 
 
 
 
Éducation Nationale
Mathematics Teacher
Éducation Nationale
September 2007 – August 2008 Aubervilliers, France
Teaching mathematics in secondary education

Recent Publications

Milan Bhan, Jean-Noël Vittaut, Nina Achache, Victor Legrand, Annabelle Blangero, Nicolas Chesneau, Juliette Murris, Marie-Jeanne Lesot (2026). Mitigating Text Toxicity with Counterfactual Generation. Explainable Artificial Intelligence.

Cite

Milan Bhan, Yann Choho, Jean-Noël Vittaut, Nicolas Chesneau, Pierre Moreau, Marie-Jeanne Lesot (2025). Towards Achieving Concept Completeness for Textual Concept Bottleneck Models. Findings of the Association for Computational Linguistics: EMNLP 2025.

Cite DOI URL

Thomas Pontoizeau, Romain Caillière, Marie-Jeanne Lesot, Christophe Marsala, Nicolas Museux, Jean-Noël Vittaut (2025). Assessing the Interpretability of Fuzzy Rule Bases. 2025 IEEE International Conference on Fuzzy Systems (FUZZ).

Cite DOI

Milan Bhan, Jean-Noël Vittaut, Nicolas Chesneau, Marie-Jeanne Lesot (2024). Self-AMPLIFY: Improving Small Language Models with Self Post Hoc Explanations. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024).

Cite DOI URL

Louis Serrano, Thomas X Wang, Etienne Le Naour, Jean-Noël Vittaut, Patrick Gallinari (2024). AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields. Advances in Neural Information Processing Systems (NeurIPS 2024).

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