Projects per year
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.
Original language | English |
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Title of host publication | The 22nd International Conference on Artificial Intelligence and Statistics |
Pages | 1812-1821 |
Publication status | Published - May 2019 |
MoE publication type | A4 Article in a conference publication |
Event | International Conference on Artificial Intelligence and Statistics - Naha, Japan Duration: 16 Apr 2019 → 18 Apr 2019 Conference number: 22 |
Publication series
Name | Proceedings of Machine Learning Research |
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Publisher | PMLR |
Volume | 89 |
ISSN (Electronic) | 2640-3498 |
Conference
Conference | International Conference on Artificial Intelligence and Statistics |
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Abbreviated title | AISTATS |
Country/Territory | Japan |
City | Naha |
Period | 16/04/2019 → 18/04/2019 |
Keywords
- Kernel methods
- Gaussian Processes
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Dive into the research topics of 'Harmonizable mixture kernels with variational Fourier features'. Together they form a unique fingerprint.Projects
- 3 Finished
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Interactive machine learning from multiple biodata sources
Jälkö, J., Hegde, P., Kaski, S., Gadd, C., Jain, A., Hämäläinen, A., Siren, J., Shen, Z. & Trinh, T.
01/01/2019 → 31/08/2021
Project: Academy of Finland: Other research funding
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Next-generation statistical learning for synthetic enzyme engineering
01/09/2016 → 31/08/2019
Project: Academy of Finland: Other research funding
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Interactive machine learning from multiple biodata sources
Sundin, I., Hegde, P., Eranti, P., Kaski, S., Reinvall, J., Jälkö, J., Aushev, A., Celikok, M. M., Kangas, J., Honkamaa, J., Afrabandpey, H., Daee, P., Blomstedt, P., Chen, Y., Qin, X., Shen, Z., Peltola, T., Pesonen, H. & Siren, J.
01/01/2016 → 31/12/2018
Project: Academy of Finland: Other research funding