AI RESEARCH
Amortized Linear-time Exact Shapley Value for Product-Kernel Methods
arXiv CS.LG
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ArXi:2505.16516v3 Announce Type: replace Kernel methods are widely used in machine learning and statistics for their flexibility and expressive power, yet their black-box nature limits adoption in high-stakes applications. Shapley value-based attribution methods such as SHAP, and kernel-specific adaptations including RKHS-SHAP, provide a principled framework for explainability -- but exact computation of Shapley values is generally intractable, forcing existing approaches to rely on approximations that incur unavoidable estimation error. We.