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arXiv · 2410.06300

SHAP values via sparse Fourier representation

Ali Gorji·Andisheh Amrollahi·Andreas Krause
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Abstract

SHAP (SHapley Additive exPlanations) values are a widely used method for local feature attribution in interpretable and explainable AI. We propose an efficient two-stage algorithm for computing SHAP values in both black-box setting and tree-based models. Motivated by spectral bias in real-world predictors, we first approximate models using compact Fourier representations, exactly for trees and approximately for black-box models. In the second stage, we introduce a closed-form formula for {\em exactly} computing SHAP values using the Fourier representation, that ``linearizes'' the computation into a simple summation and is amenable to parallelization. As the Fourier approximation is computed only once, our method enables amortized SHAP value computation, achieving significant speedups over existing methods and a tunable trade-off between efficiency and precision.

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Ali Gorji, Andisheh Amrollahi, Andreas Krause. 2024-10-08. SHAP values via sparse Fourier representation. https://arxiv.org/abs/2410.06300

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