Product Kernel Interpolation for Scalable Gaussian Processes
Abstract
Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs). Structured Kernel Interpolation (SKI) exploits these techniques by deriving approximate kernels with very fast MVMs. Unfortunately, such strategies suffer badly from the curse of dimensionality. We develop a new technique for MVM based learning that exploits product kernel structure. We demonstrate that this technique is broadly applicable, resulting in linear rather than exponential runtime with dimension for SKI, as well as state-of-the-art asymptotic complexity for multi-task GPs.
Cite
Text
Gardner et al. "Product Kernel Interpolation for Scalable Gaussian Processes." International Conference on Artificial Intelligence and Statistics, 2018.Markdown
[Gardner et al. "Product Kernel Interpolation for Scalable Gaussian Processes." International Conference on Artificial Intelligence and Statistics, 2018.](https://mlanthology.org/aistats/2018/gardner2018aistats-product/)BibTeX
@inproceedings{gardner2018aistats-product,
title = {{Product Kernel Interpolation for Scalable Gaussian Processes}},
author = {Gardner, Jacob R. and Pleiss, Geoff and Wu, Ruihan and Weinberger, Kilian Q. and Wilson, Andrew Gordon},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2018},
pages = {1407-1416},
url = {https://mlanthology.org/aistats/2018/gardner2018aistats-product/}
}