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)

A survey of Identification and mitigation of Machine Learning algorithmic biases in Image Analysis. - Laurent Risser; Agustin Martin Picard; Lucas Hervier; Jean-Michel Loubes (CoRR, 2022)

COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP. - Fanny Jourdan; Agustin Martin Picard; Thomas Fel; Laurent Risser; Jean-Michel Loubes; Nicholas Asher (ACL (Findings), 2023)

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)

Detecting and Processing Unsuspected Sensitive Variables for Robust Machine Learning. - Laurent Risser; Agustin Martin Picard; Lucas Hervier; Jean-Michel Loubes (Algorithms, 2023)

COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasks. - Fanny Jourdan; Agustin Martin Picard; Thomas Fel; Laurent Risser; Jean-Michel Loubes; Nicholas Asher (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)

ConSim: Measuring Concept-Based Explanations' Effectiveness with Automated Simulatability. - Antonin Poché; Alon Jacovi; Agustin Martin Picard; Victor Boutin; Fanny Jourdan (ACL (1), 2025)

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)