A Weighted Kernel Method for Approximation that Adapts to Learned Multivariable Structure
Approximating the input-output behavior of a multivariable black-box function from limited data is challenging when blind to the importance of its inputs and their interactions. We introduce total sensitivity kernels (TSKs), a method based on families of weighted ANOVA kernels that learn and adapt to this multivariable structure. TSKs parameterize the weights on each multivariable component of the target function by factors for each input. We propose learning these factors directly from function evaluations by selecting the reproducing kernel Hilbert space (RKHS) in which the target function has minimum norm. Under suitable conditions, we show that this norm-minimization problem admits a ...
| Authors | John E. Darges, Laura Weidensager |
|---|---|
| Primary category | cs.LG |
| Published | 15 September 2026 |
| Last updated at source | 25 September 2026 |
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