用傅里叶表示加速SHAP值计算，兼顾精度与效率
SHAP values via sparse Fourier representation
- 用紧凑傅里叶表示近似模型，适配树模型与黑盒模型
- 提出闭式公式，将SHAP计算转化为可并行的求和运算
- 一次傅里叶近似后可重复使用，实现计算成本摊销
SHAP（SHapley Additive exPlanations）值是可解释人工智能中广泛使用的局部特征归因方法。本文提出一种高效的两阶段算法，用于在黑盒设置和基于树的模型中计算SHAP值。受真实世界预测器的谱偏置启发，首先利用紧凑的傅里叶表示近似模型，对树模型精确近似，对黑盒模型近似处理。第二阶段引入闭式公式，通过傅里叶表示精确计算SHAP值，将计算过程线性化为简单求和，便于并行化。由于傅里叶近似仅需计算一次，本方法实现SHAP值的摊销计算，在显著提升速度的同时，提供效率与精度间的可调权衡。
原文摘要 · Abstract (English)
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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