Title: SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation
Primary area: interpretability and explainable AI
Keywords: Shapley values, Explainable AI, Stochastic optimization, Feature attribution, Sample average approximation, Momentum methods

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 IterativeMomentum 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://anonymous.4open.science/r/SIM-Shapley.