Can I read Product Kernel Interpolation for Scalable Gaussian Processes on EtoBox?

Product Kernel Interpolation for Scalable Gaussian Processes by Gardner, Jacob R.; Pleiss, Geoff; Wu, Ruihan; Weinberger, Kilian Q.; Wilson, Andrew Gordon is a scholarly article available to read on EtoBox.

What is Product Kernel Interpolation for Scalable Gaussian Processes about?

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.

Author

Gardner, Jacob R.; Pleiss, Geoff; Wu, Ruihan; Weinberger, Kilian Q.; Wilson, Andrew Gordon

Published

2018

Language

EN