Conference item
Marginalising over stationary kernels with Bayesian quadrature
- Abstract:
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Marginalising over families of Gaussian Process kernels produces flexible model classes with well-calibrated uncertainty estimates. Existing approaches require likelihood evaluations of many kernels, rendering them prohibitively expensive for larger datasets. We propose a Bayesian Quadrature scheme to make this marginalisation more efficient and thereby more practical. Through use of maximum mean discrepancies between distributions, we define a kernel over kernels that captures invariances be...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Version of record, pdf, 10.6MB)
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- Publication website:
- https://proceedings.mlr.press/v151/hamid22a.html
Authors
Bibliographic Details
- Publisher:
- Journal of Machine Learning Research Publisher's website
- Series:
- Proceedings of Machine Learning Research
- Series number:
- 151
- Pages:
- 9776-9792
- Publication date:
- 2022-05-03
- Acceptance date:
- 2022-01-29
- Event title:
- International Conference on Artificial Intelligence and Statistics (AISTATS 2022)
- Event location:
- Virtual event
- Event website:
- https://aistats.org/aistats2022/
- Event start date:
- 2022-03-28
- Event end date:
- 2022-03-30
- ISSN:
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2640-3498
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1288051
- Local pid:
- pubs:1288051
- Deposit date:
- 2023-01-20
Terms of use
- Copyright holder:
- Hamid et al.
- Copyright date:
- 2022
- Rights statement:
- Copyright 2022 by the author(s).
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