SIRUS: making random forests interpretable. - Clément Bénard; Gérard Biau; Sébastien Da Veiga; Erwan Scornet (CoRR, 2019)

Interpretable Random Forests via Rule Extraction. - Clément Bénard; Gérard Biau; Sébastien Da Veiga; Erwan Scornet (AISTATS, 2021)

MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA. - Clément Bénard; Sébastien Da Veiga; Erwan Scornet (CoRR, 2021)

Forêts aléatoires et interprétabilité des algorithmes d'apprentissage. (Random forests and interpretability of learning algorithms). - Clément Bénard (2021)

SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. - Clément Bénard; Gérard Biau; Sébastien Da Veiga; Erwan Scornet (AISTATS, 2022)

Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization. - Clément Bénard; Brian Staber; Sébastien Da Veiga (NeurIPS, 2023)

MMD-based Variable Importance for Distributional Random Forest. - Clément Bénard; Jeffrey Näf; Julie Josse (AISTATS, 2024)