Title: STRIDE: Subset-Free Functional Decomposition for XAI in Tabular Settings
Primary area: interpretability and explainable AI
Keywords: Explainable AI (XAI), Functional Decomposition, Feature Interaction, Kernel Methods, Scalable Explainability, Reproducing Kernel Hilbert Space (RKHS)

Abstract: Most explainable AI (XAI) frameworks are limited in their expressiveness, summarizing complex feature effects as single scalar values ($\phi_i$). This approach answers \emph{what} features are important but fails to reveal \emph{how} they interact. Furthermore, methods that attempt to capture interactions, like those based on Shapley values, often face an exponential computational cost. We present STRIDE, a scalable framework that addresses both limitations by reframing explanation as a subset-enumeration-free, orthogonal \textbf{functional decomposition} in a Reproducing Kernel Hilbert Space (RKHS). In the tabular setups we study, STRIDE analytically computes functional components $f_S(x_S)$ via a recursive kernel-centering procedure. The approach is model-agnostic and theoretically grounded with results on orthogonality and $L^2$ convergence. In tabular benchmarks (10 datasets, median over 10 seeds), STRIDE attains a 3.0$\times$ median speedup over TreeSHAP and a mean $R^2=0.93$ for reconstruction. We also introduce \textbf{component surgery}, a diagnostic that isolates a learned interaction and quantifies its contribution; on California Housing, removing a single interaction reduces test $R^2$ by $0.023\pm0.004$.