Kernel interpolation for scalable structured Gaussian processes (KISS-GP)

Andrew Gordon Wilson, Hannes Nickisch

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs). SKI methods produce kernel approximations for fast computations through kernel interpolation. The SKI framework clarifies how the quality of an inducing point approach depends on the number of inducing (aka interpolation) points, interpolation strategy, and GP covariance kernel. SKI also provides a mechanism to create new scalable kernel methods, through choosing different kernel interpolation strategies. Using SKI, with local cubic kernel interpolation, we introduce KISS-GP, which is 1) more scalable than inducing point alternatives, 2) naturally enables Kronecker and Toeplitz algebra for substantial additional gains in scalability, without requiring any grid data, and 3) can be used for fast and expressive kernel learning. KISS-GP costs O(n) time and storage for GP inference. We evaluate KISS-GP for kernel matrix approximation, kernel learning, and natural sound modelling.

Original languageEnglish (US)
Title of host publication32nd International Conference on Machine Learning, ICML 2015
EditorsFrancis Bach, David Blei
PublisherInternational Machine Learning Society (IMLS)
Pages1775-1784
Number of pages10
ISBN (Electronic)9781510810587
StatePublished - 2015
Event32nd International Conference on Machine Learning, ICML 2015 - Lile, France
Duration: Jul 6 2015Jul 11 2015

Publication series

Name32nd International Conference on Machine Learning, ICML 2015
Volume3

Other

Other32nd International Conference on Machine Learning, ICML 2015
CountryFrance
CityLile
Period7/6/157/11/15

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Science Applications

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  • Cite this

    Wilson, A. G., & Nickisch, H. (2015). Kernel interpolation for scalable structured Gaussian processes (KISS-GP). In F. Bach, & D. Blei (Eds.), 32nd International Conference on Machine Learning, ICML 2015 (pp. 1775-1784). (32nd International Conference on Machine Learning, ICML 2015; Vol. 3). International Machine Learning Society (IMLS).