Negrevergne Benjamin - CV

LAMSADE

Negrevergne Benjamin

Associate Professor

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Phone : +331 44 05 44 18

Office : P407

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

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

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