LAMSADE
Negrevergne Benjamin
Associate Professor
Biography
I am associate professor at the Lamsade laboratory (Dauphine – PSL University), in Paris (France).
I am an active member of the MILES project and my research is about Machine Learning, Data Mining and Constraint Programming. My main research focuses on the links between machine learning and combinatorial optimization (I am the PI of the DELCO ANR project on this topic). I have also been looking at a variety of other problems such as the design of neural networks that are robust against advesarial attacks, or the usage of structured matrices inside neural networks.
Finally, I am also co-director of the IASD Mater Program (AI Systems and Data Science).
Publications
Communications avec actes
Gnecco Heredia L., Pydi M., Meunier L., Negrevergne B., Chevaleyre Y. (2023), On the Role of Randomization in Adversarially Robust Classification, in A. Oh , T. Neumann, A. Globerson, K. Saenko, M. Hardt, S. Levine, Neural Information Processing Systems Foundation, Inc.
Doux B., Negrevergne B., Cazenave T. (2022), Deep Reinforcement Learning for Morpion Solitaire, in Cameron Browne ; Akihiro Kishimoto ; Jonathan Schaeffer, Berlin Heidelberg, Springer International Publishing, 14-26 p. 
Araujo A., Meunier L., Pinot R., Negrevergne B. (2020), Advocating for Multiple Defense Strategies against Adversarial Examples, in Irena Koprinska, Michael Kamp, Annalisa Appice, Berlin Heidelberg, Springer International Publishing
Araújo A., Negrevergne B., Chevaleyre Y., Atif J. (2018), Training Compact Deep Learning Models for Video Classification Using Circulant Matrices, in Leal-Taixé Laura; Roth Stefan, Computer Vision – ECCV 2018 Workshops Munich, Germany, September 8-14, 2018, Proceedings, Part IV, Berlin Heidelberg, Springer, 271-286 p.
Samet A., Guyet T., Negrevergne B. (2017), Mining rare sequential patterns with ASP, in N. Lachiche, C. Vrain, 27th International Conference on Inductive Logic Programming (ILP 2017), Orléans, Centre International Universitaire pour la Recherche
Negrevergne B., Cazenave T. (2017), Distributed Nested Rollout Policy for Same Game, in Tristan Cazenave, Mark H.M. Winands, Abdallah Saffidine, Computer Games, 6th Workshop, CGW 2017, Springer, 108-120 p.
Lecoutre A., Negrevergne B., Yger F. (2017), Recognizing Art Style Automatically in painting with deep learning, in Yung-Kyun Noh, Min-Ling Zhang, Proceedings of the 9th Asian Conference on Machine Learning (ACML 2017), IEEE - Institute of Electrical and Electronics Engineers, 327-342 p.
Communications sans actes
Verine A., Negrevergne B., Rossi F., Chevaleyre Y. (2022), On the expressivity of bi-Lipschitz normalizing flows, ACML 2022 - 14th Asian Conference on Machine Learning, Hyderabad, Inde
Araújo A., Negrevergne B., Chevaleyre Y., Atif J. (2021), On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory, 35th AAAI Conference on Artificial Intelligence, vancouver, Canada
Boria N., Negrevergne B., Yger F. (2020), Fréchet Mean Computation in Graph Space through Projected Block Gradient Descent, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2020), Bruges, France
Prépublications / Cahiers de recherche
Araújo A., Negrevergne B., Chevaleyre Y., Atif J. (2019), On the Expressive Power of Deep Fully Circulant Neural Networks, Paris, Preprint Lamsade