1 citations · 1 across the 4 of their papers we have counts for
5 papers
A Weighted Kernel Method for Approximation that Adapts to Learned Multivariable Structure
John E. Darges, Laura Weidensager
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…
Approximating the Fourier Transform from Non-equispaced Discrete Samples
Daniel Potts, Laura Weidensager
We study the approximation of the Fourier transform of a function from finitely many samples. Departing from the equispaced setting, we sample the function at deterministic non-equ…
Sensitivity Analysis on the Sphere and a Spherical ANOVA Decomposition
Laura Weidensager
We establish sensitivity analysis on the sphere. We present formulas that allow us to decompose a function into a sum of terms $f_{\bolds…
ANOVA-boosting for Random Fourier Features
Daniel Potts, Laura Weidensager
We propose two algorithms for boosting random Fourier feature models for approximating high-dimensional functions. These methods utilize the classical and generalized analysis of v…
Variable Transformations in combination with Wavelets and ANOVA for high-dimensional approximation
Daniel Potts, Laura Weidensager
We use hyperbolic wavelet regression for the fast reconstruction of high-dimensional functions having only low dimensional variable interactions. Compactly supported periodic Chui-…