Exploring Unified Perspective For Fast Shapley Value Estimation
Borui Zhang Baotong Tian Wenzhao Zheng Jie Zhou Jiwen Lu
Shapley values have emerged as a widely accepted and trustworthy tool,
grounded in theoretical axioms, for addressing challenges posed by black-box
models like deep neural networks. However, computing Shapley values encounters
exponential complexity in the number of features. Various approaches, including
ApproSemivalue, KernelSHAP, and FastSHAP, have been explored to expedite the
computation. We analyze the consistency of existing works and conclude that
stochastic estimators can be unified as the linear transformation of importance
sampling of feature subsets. Based on this, we investigate the possibility of
designing simple amortized estimators and propose a straightforward and
efficient one, SimSHAP, by eliminating redundant techniques. Extensive
experiments conducted on tabular and image datasets validate the effectiveness
of our SimSHAP, which significantly accelerates the computation of accurate
Shapley values.