Baptiste Ferrere
About Me
I am a Ph.D. student in Applied Mathematics at the Université de Toulouse, in collaboration with EDF R&D, where I am working on eXplainable AI (XAI).
My research is supervised by Fabrice Gamboa and Jean-Michel Loubes (Université de Toulouse), and Nicolas Bousquet and Joseph Muré (EDF R&D).
Before starting my Ph.D., I completed a 6-month research internship at EDF R&D focusing on the generalization of the functional ANOVA decomposition for correlated inputs.
I also hold an M.Sc. in Statistical Learning from the Institut Polytechnique de Paris, and an engineering degree from ENSAE Paris, where I specialized in high dimensional statistics and machine learning theory.
News
- (05/2026) Our preprint, Generalized Functional ANOVA in Closed-Form: A Unified View of Additive Explanations is now available on arXiv!
- (04/2026) Our paper, Exact Functional ANOVA Decomposition for Categorical Inputs Models has been accepted to ICML 2026 as a spotlight paper (top 2%)!
- (03/2026) Our preprint, Exact Functional ANOVA Decomposition for Categorical Inputs Models is now available on arXiv!
- (10/2025) Our preprint, Fourier Analysis on the Boolean Hypercube via Hoeffding Functional Decomposition is now available on arXiv!
Research Interests
My research lies at the intersection of applied mathematics and machine learning. I focus on:
- Interpretability of black-box machine learning models
- Functional Analysis (e.g. Hilbert space methods)
- Additive Explanations (e.g. Shapley Values, Functional ANOVA)
I am particularly interested in mathematically grounded methods that improve our understanding of ML models and help build trust in AI systems.