Prediction Via Shapley Value Regression

ICLR 2025 Conference Submission329 Authors

Keywords: Explainable Machine Learning, Neural Networks, Kolmogorov–Arnold Networks

Abstract: Shapley values have several desirable properties for explaining black-box model predictions, which come with strong theoretical support. Traditionally, Shapley values are computed post-hoc, leading to additional computational cost at inference time. To overcome this, we introduce ViaSHAP, a novel approach that learns a function to compute Shapley values, from which the predictions can be derived directly by summation. We explore two learning approaches based on the universal approximation theorem and the Kolmogorov-Arnold representation theorem. Results from a large-scale empirical investigation are presented, in which the predictive performance of ViaSHAP is compared to state-of-the-art algorithms for tabular data, where the implementation using Kolmogorov-Arnold Networks showed a superior performance. It is also demonstrated that the explanations of ViaSHAP are accurate, and that the accuracy is controllable through the hyperparameters.

Primary Area: interpretability and explainable AI

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Submission Number: 329