Structure Discovery in Nonparametric Regression Through Compositional Kernel Search
Abstract
Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art. We define a space of kernel structures which are built compositionally by adding and multiplying a small number of base kernels. We present a method for searching over this space of structures which mirrors the scientific discovery process. The learned structures can often decompose functions into interpretable components and enable long-range extrapolation on time-series datasets. Our structure search method outperforms many widely used kernels and kernel combination methods on a variety of prediction tasks.
Cite
Text
Duvenaud et al. "Structure Discovery in Nonparametric Regression Through Compositional Kernel Search." International Conference on Machine Learning, 2013.Markdown
[Duvenaud et al. "Structure Discovery in Nonparametric Regression Through Compositional Kernel Search." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/duvenaud2013icml-structure/)BibTeX
@inproceedings{duvenaud2013icml-structure,
title = {{Structure Discovery in Nonparametric Regression Through Compositional Kernel Search}},
author = {Duvenaud, David and Lloyd, James and Grosse, Roger and Tenenbaum, Joshua and Zoubin, Ghahramani},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {1166-1174},
volume = {28},
url = {https://mlanthology.org/icml/2013/duvenaud2013icml-structure/}
}