Generalized Functional ANOVA: A Complete Theoretical Framework

arXiv:2605.18422 · stat.ML, cs.LG, math.ST, stat.TH · Submitted 2026-05-18 · Read on arXiv

stat.ML, cs.LG, math.ST, stat.TH

Submitted: 2026-05-18

Updated: 2026-09-26

Code: https://github.com/shap/shap

Terminology

• Shapley values — 10× in this paper · explained in 2 episodes
• Explainability — 8× in this paper · explained in 3 episodes
• Interpretability — 7× in this paper · explained in 15 episodes
• Model-Agnostic — 6× in this paper · explained in 2 episodes
• Hilbert Space — 5× in this paper · explained in 2 episodes
• Sparsity — 5× in this paper · explained in 4 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• SHAP values — 4× in this paper · explained in 2 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Explainable Boosting Machine — 3× in this paper
• Additive Decomposition — 2× in this paper · explained in 1 episode
• Curse of Dimensionality — 2× in this paper · explained in 6 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Uncertainty Quantification — 2× in this paper · explained in 8 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Axioms — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Copula — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Explainable AI (XAI) — 1× in this paper · explained in 7 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning
• Exact Functional ANOVA Decomposition for Categorical Inputs Models
• Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation
• Adam: A Method for Stochastic Optimization
• Consistent Individualized Feature Attribution for Tree Ensembles
• InterpretML: A Unified Framework for Machine Learning Interpretability
• Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
• Reluctant Interaction Modeling

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