Melanie Ducoffe — accepted AI-conference papers

2026

FAME: $\underline{F}$ormal $\underline{A}$bstract $\underline{M}$inimal $\underline{E}$xplanation for neural networks

Ryma Boumazouza, Raya Elsaleh, Melanie Ducoffe, Shahaf Bassan, Guy Katz

ICLR 2026poster

We propose $\textbf{FAME}$ (Formal Abstract Minimal Explanations), a new class of abductive explanations grounded in abstract interpretation. FAME is the first method to scale to large neural networks while reducing explanation size. Our main contribution is the design of dedicated perturbation doma…

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2023

Don't Lie to Me! Robust and Efficient Explainability With Verified Perturbation Analysis

Thomas Fel, Melanie Ducoffe, David Vigouroux, Rémi Cadène, Mikaël Capelle, Claire Nicodème +1

CVPR 2023poster

A variety of methods have been proposed to try to explain how deep neural networks make their decisions. Key to those approaches is the need to sample the pixel space efficiently in order to derive importance maps. However, it has been shown that the sampling methods used to date introduce biases an…

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