SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation

• URL: http://arxiv.org/abs/2505.08198v1
• Date: Tue, 13 May 2025 03:23:10 GMT
• Title: SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation
• Authors: Wangxuan Fan, Siqi Li, Doudou Zhou, Yohei Okada, Chuan Hong, Molei Liu, Nan Liu,
• Abstract summary: Shapley value (SV) methods provide a principled framework for feature attribution in complex models but incur high computational costs.<n>We propose Iterative Momentum for Shapley Value Approximation (SIM-Shapley), a stable and efficient approximation method inspired by optimization.<n>In our numerical experiments, SIM-Shapley reduces computation time by up to 85% relative to state-of-the-art baselines.
• Score: 8.323065815365602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Explainable artificial intelligence (XAI) is essential for trustworthy machine learning (ML), particularly in high-stakes domains such as healthcare and finance. Shapley value (SV) methods provide a principled framework for feature attribution in complex models but incur high computational costs, limiting their scalability in high-dimensional settings. We propose Stochastic Iterative Momentum for Shapley Value Approximation (SIM-Shapley), a stable and efficient SV approximation method inspired by stochastic optimization. We analyze variance theoretically, prove linear $Q$-convergence, and demonstrate improved empirical stability and low bias in practice on real-world datasets. In our numerical experiments, SIM-Shapley reduces computation time by up to 85% relative to state-of-the-art baselines while maintaining comparable feature attribution quality. Beyond feature attribution, our stochastic mini-batch iterative framework extends naturally to a broader class of sample average approximation problems, offering a new avenue for improving computational efficiency with stability guarantees. Code is publicly available at https://github.com/nliulab/SIM-Shapley.

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