Scaling up the Automatic Statistician: Scalable Structure Discovery Using Gaussian Processes
Abstract
Automating statistical modelling is a challenging problem in artificial intelligence. The Automatic Statistician takes a first step in this direction, by employing a kernel search algorithm with Gaussian Processes (GP) to provide interpretable statistical models for regression problems. However this does not scale due to its $O(N^3)$ running time for the model selection. We propose Scalable Kernel Composition (SKC), a scalable kernel search algorithm that extends the Automatic Statistician to bigger data sets. In doing so, we derive a cheap upper bound on the GP marginal likelihood that sandwiches the marginal likelihood with the variational lower bound . We show that the upper bound is significantly tighter than the lower bound and thus useful for model selection.
Cite
Text
Kim and Teh. "Scaling up the Automatic Statistician: Scalable Structure Discovery Using Gaussian Processes." International Conference on Artificial Intelligence and Statistics, 2018.Markdown
[Kim and Teh. "Scaling up the Automatic Statistician: Scalable Structure Discovery Using Gaussian Processes." International Conference on Artificial Intelligence and Statistics, 2018.](https://mlanthology.org/aistats/2018/kim2018aistats-scaling/)BibTeX
@inproceedings{kim2018aistats-scaling,
title = {{Scaling up the Automatic Statistician: Scalable Structure Discovery Using Gaussian Processes}},
author = {Kim, Hyunjik and Teh, Yee Whye},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2018},
pages = {575-584},
url = {https://mlanthology.org/aistats/2018/kim2018aistats-scaling/}
}