Achieving Robustness in Classification Using Optimal Transport With Hinge Regularization. - Mathieu Serrurier; Franck Mamalet; Alberto González-Sanz; Thibaut Boissin; Jean-Michel Loubes; Eustasio del Barrio (CVPR, 2021)

Pay attention to your loss : understanding misconceptions about Lipschitz neural networks. - Louis Béthune; Thibaut Boissin; Mathieu Serrurier; Franck Mamalet; Corentin Friedrich; Alberto González-Sanz (NeurIPS, 2022)

Xplique: A Deep Learning Explainability Toolbox. - Thomas Fel; Lucas Hervier; David Vigouroux; Antonin Poché; Justin Plakoo; Rémi Cadène; Mathieu Chalvidal; Julien Colin; Thibaut Boissin; Louis Béthune; Agustin Martin Picard; Claire Nicodeme; et al. (CoRR, 2022)

When adversarial attacks become interpretable counterfactual explanations. - Mathieu Serrurier; Franck Mamalet; Thomas Fel; Louis Béthune; Thibaut Boissin (CoRR, 2022)

CRAFT: Concept Recursive Activation FacTorization for Explainability. - Thomas Fel; Agustin Martin Picard; Louis Béthune; Thibaut Boissin; David Vigouroux; Julien Colin; Rémi Cadène; Thomas Serre (CVPR, 2023)

Robust One-Class Classification with Signed Distance Function using 1-Lipschitz Neural Networks. - Louis Béthune; Paul Novello; Guillaume Coiffier; Thibaut Boissin; Mathieu Serrurier; Quentin Vincenot; Andres Troya-Galvis (ICML, 2023)

Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization. - Thomas Fel; Thibaut Boissin; Victor Boutin; Agustin Picard; Paul Novello; Julien Colin; Drew Linsley; Tom Rousseau; Rémi Cadène; Lore Goetschalckx; Laurent Gardes; Thomas Serre (NeurIPS, 2023)

On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective. - Mathieu Serrurier; Franck Mamalet; Thomas Fel; Louis Béthune; Thibaut Boissin (NeurIPS, 2023)

Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception. - Drew Linsley; Pinyuan Feng; Thibaut Boissin; Alekh Karkada Ashok; Thomas Fel; Stephanie Olaiya; Thomas Serre (CoRR, 2023)

Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization. - Thomas Fel; Thibaut Boissin; Victor Boutin; Agustin Martin Picard; Paul Novello; Julien Colin; Drew Linsley; Tom Rousseau; Rémi Cadène; Laurent Gardes; Thomas Serre (CoRR, 2023)

DP-SGD Without Clipping: The Lipschitz Neural Network Way. - Louis Béthune; Thomas Massena; Thibaut Boissin; Aurélien Bellet; Franck Mamalet; Yannick Prudent; Corentin Friedrich; Mathieu Serrurier; David Vigouroux (ICLR, 2024)

Fairness seen as global sensitivity analysis. - Clément Benesse; Fabrice Gamboa; Jean-Michel Loubes; Thibaut Boissin (Mach. Learn., 2024)

An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures. - Thibaut Boissin; Franck Mamalet; Thomas Fel; Agustin Martin Picard; Thomas Massena; Mathieu Serrurier (CoRR, 2025)

Efficient Robust Conformal Prediction via Lipschitz-Bounded Networks. - Thomas Massena; Léo Andéol; Thibaut Boissin; Franck Mamalet; Corentin Friedrich; Mathieu Serrurier; Sébastien Gerchinovitz (CoRR, 2025)