Kolmogorov n-width theory plus PRESS statistics yield closed-form optimal spline resolution; KORE estimates bias/noise scales from two pilots and matches CV performance with far fewer fits.
arXiv preprint arXiv:2106.01613 , year=
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Introduces a Riesz basis for explicit generalized functional ANOVA decomposition under input dependence and an associated data-driven estimation procedure.
ParamBoost improves GAMs by fitting piecewise cubic polynomials via gradient boosting and supports constraints for continuity, monotonicity, convexity, and feature interactions.
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Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression
Kolmogorov n-width theory plus PRESS statistics yield closed-form optimal spline resolution; KORE estimates bias/noise scales from two pilots and matches CV performance with far fewer fits.
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Generalized Functional ANOVA in Closed-Form: A Unified View of Additive Explanations
Introduces a Riesz basis for explicit generalized functional ANOVA decomposition under input dependence and an associated data-driven estimation procedure.
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ParamBoost: Gradient Boosted Piecewise Cubic Polynomials
ParamBoost improves GAMs by fitting piecewise cubic polynomials via gradient boosting and supports constraints for continuity, monotonicity, convexity, and feature interactions.