Product Kernel Interpolation for Scalable Gaussian Processes
Machine Learning
2018-02-27 v1 Machine Learning
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
@article{arxiv.1802.08903,
title = {Product Kernel Interpolation for Scalable Gaussian Processes},
author = {Jacob R. Gardner and Geoff Pleiss and Ruihan Wu and Kilian Q. Weinberger and Andrew Gordon Wilson},
journal= {arXiv preprint arXiv:1802.08903},
year = {2018}
}
Comments
Appears in Artificial Intelligence and Statistics (AISTATS) 21, 2018