I am currently an AI research scientist at Thales Research, in Paris region. My research interests span explainable machine learning (XAI), causal inference, tree ensembles, decision aid, deep learning, uncertainty quantification, and active learning. I am also driven by practical applications and software development.

I have ten years of experience as a machine learning researcher and data scientist in aeronautic/aerospace and tech industries, with also a strong interest in healthcare applications. I hold a PhD in machine learning and mathematical statistics, on the topic of explainable and interpretable ML (XAI). In my research, I address both the design of new algorithms, and the theoretical analysis of their mathematical properties.

My resume is available here.

Research interests

• Explainable Machine Learning - XAI (variable importance, functional decomposition, rule learning)
• Causal Inference (heterogeneous treatment effects, causal forests, causal discovery)
• Tree Ensembles (random forests, boosted trees)
• Decision Aid (MCDA, Choquet integral)
• Deep Learning (GNN, Neur-HCI)
• Uncertainty Quantification (Bayesian inference, conformal predictions)
• Active Learning (design of experiments, Bayesian optimization)

Publications

Preprints

• C. Bénard, J. Josse. Variable importance for causal forests: breaking down the heterogeneity of treatment effects. arXiv preprint arXiv:2308.03369, 2023.

Published/Accepted papers

• C. Bénard, J. Näf, and J. Josse. MMD-based Variable Importance for Distributional Random Forest. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), PMLR 238:1324-1332, 2024.
• C. Bénard, B. Staber, and S. Da Veiga. Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023.
• C. Bénard, S. Da Veiga, and E. Scornet. Mean decrease accuracy for random forests: inconsistency, and a practical solution via the Sobol-MDA. Biometrika, 109:881-900, 2022.
• C. Bénard, G. Biau, S. Da Veiga, and E. Scornet. SHAFF: Fast and consistent SHApley eFfect estimates via random Forests. In Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), PMLR 151:5563-5582, 2022.
• C. Bénard, G. Biau, S. Da Veiga, and E. Scornet. Interpretable random forests via rule extraction. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), PMLR 130:937–945, 2021.
• C. Bénard, G. Biau, S. Da Veiga, and E. Scornet. SIRUS: Stable and Interpretable RUle Set for classification. Electronic Journal of Statistics, 15:427–505, 2021.

Book

• B. Iooss, R. Kenett, P. Secchi, B.M. Colosimo, F. Centofanti, C. Bénard, S. Da Veiga, E. Scornet, S. N. Wood, Y. Goude, M. Fasiolo. Interpretability for Industry 4.0: Statistical and Machine Learning Approaches, Editors: A. Lepore, B. Palumbo, J.-M. Poggi, Springer 2022.

Academic publications

• PhD thesis Random forests and interpretability of learning algorithms, 6 December 2021. Supervisors : Gérard Biau, Erwan Scornet, and Sébastien Da Veiga.

Software

• Kernax: Kernax is a package that implements KSD-based algorithms for post-processing MCMC outputs. It is based on JAX and works on CPU as well as GPU.
• Lagun: platform providing a user-friendly interface to methods and algorithms dedicated to the exploration of numerical simulations and the analysis of datasets (design of experiments, conditional gaussian processes, sensitivity analysis, uncertainty propagation, optimization).
• sirus (Stable and Interpretable RUle Set): a regression and classification algorithm based on random forests, which takes the form of a short list of rules. SIRUS combines the simplicity of decision trees with a predictivity close to random forests.
• shaff (SHApley eFfects via random Forests): a fast and accurate algorithm to estimate Shapley effects.
• sobolMDA: the Sobol-MDA is a variable importance measure for random forests, fixing the flaws of Breiman’s MDA.
• vimp-causal-forests: a variable importance measure for causal forests.

Talks

• March 2024, SIAM UQ 2024, Trieste, Italy.
• December 2023, NeurIPS@Paris, Sorbonne Université, Paris.
• December 2023, 2nd Nice Workshop on Interpretability, Laboratoire J.A. Dieudonné, Université Côte d’Azur, Nice.
• August 2023, EcoSta 2023, Tokyo, Japan.
• March 2023, CSMA Junior, Paris.
• December 2022, CMStatistics 2022, King’s College, London, UK.
• March 2022, AISTATS 2022, virtual conference.
• March 2022, Séminaire de Probabilités et Statistiques, Laboratoire de Mathématiques d’Orsay, Université Paris-Saclay, Orsay.
• January 2022, Séminaire de Probabilités et Statistiques, Laboratoire J.A. Dieudonné, Université Côte d’Azur, Nice.
• September 2021, École Thématique sur les Incertitudes en Calcul Scientifique (ETICS2021), Erdeven, France.
• July 2021, ENBIS Workshop: Interpretability of Industry 4.0, Naples, Italy.
• June 2021, 52èmes Journées de Statistiques 2021, Nice, France.
• April 2021, GDR Mascot-Num, virtual conference.
• April 2021, SIMPAS Group meeting, Centre de Mathématiques Appliquées, Ecole Polytechnique, Saclay, France.
• April 2021, AISTATS 2021, virtual conference.
• June 2019, 51èmes Journées de Statistiques 2019, Nancy, France.

Teaching

• Instructor in statistics and optimization for mechanical engineers, 3-day sessions multiple times a year since 2018 (machine learning, uncertainty quantification, design of experiments, constrained optimization…)