FAME: Formal Abstract Minimal Explanation for Neural Networks
We propose 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 domains that eliminate the need for traversal order. FAME progressively shrinks these domains and leverages LiRPA-based bounds to discard irrelevant features, ultimately converging to a formal abstract minimal explanation. To assess explanation quality, we introduce a procedure that measures the worst-case distance between an abstract minimal explanation and a true minimal explanation. This procedure combines...
| Authors | Ryma Boumazouza, Raya Elsaleh, Melanie Ducoffe et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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