Harmonizable mixture kernels with variational Fourier features
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A4 Artikkeli konferenssijulkaisussa
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Date
2019-05
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Mcode
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Language
en
Pages
1812-1821
Series
The 22nd International Conference on Artificial Intelligence and Statistic, Volume 89, Proceedings of Machine Learning Research
Abstract
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.Description
Keywords
Kernel methods, Gaussian Processes
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Citation
Shen, Z, Heinonen, M & Kaski, S 2019, Harmonizable mixture kernels with variational Fourier features . in The 22nd International Conference on Artificial Intelligence and Statistics . Proceedings of Machine Learning Research, vol. 89, JMLR, pp. 1812-1821, International Conference on Artificial Intelligence and Statistics, Naha, Japan, 16/04/2019 . < http://proceedings.mlr.press/v89/shen19c/shen19c.pdf >
