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洞見 - High-dimensional function approximation
Learning Efficient Approximations of High-Dimensional Smooth Functions from Limited Data
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.
ANOVA-Boosting for Random Fourier Features: An Interpretable Approach to High-Dimensional Function Approximation
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.
