Harmonizable mixture kernels with variational Fourier features

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The expressive power of Gaussian processes depends heavily on the choice of kernel. In this work we propose the novel harmonizable mixture kernel (HMK), a family of expressive, interpretable, non-stationary kernels derived from mixture models on the generalized spectral representation. As a theoretically sound treatment of non-stationary kernels, HMK supports harmonizable covariances, a wide subset of kernels including all stationary and many non-stationary covariances. We also propose variational Fourier features, an inter-domain sparse GP inference framework that offers a representative set of 'inducing frequencies'. We show that harmonizable mixture kernels interpolate between local patterns, and that variational Fourier features offers a robust kernel learning framework for the new kernel family.
Original languageEnglish
Title of host publicationThe 22nd International Conference on Artificial Intelligence and Statistics
Publication statusPublished - May 2019
MoE publication typeA4 Conference publication
EventInternational Conference on Artificial Intelligence and Statistics - Naha, Japan
Duration: 16 Apr 201918 Apr 2019
Conference number: 22

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)2640-3498


ConferenceInternational Conference on Artificial Intelligence and Statistics
Abbreviated titleAISTATS


  • Kernel methods
  • Gaussian Processes


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