Functions that are holomorphic in high-dimensional parameter spaces can be efficiently approximated using sparse polynomial expansions or deep neural networks, even when only limited training data is available.
The authors propose two algorithms that utilize the ANOVA decomposition to learn low-order functions with few variable interactions, enabling reliable identification of important input variables and their interactions. This approach improves the interpretability of existing random Fourier feature models.