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Inter-domain deep Gaussian processes

Abstract:

Inter-domain Gaussian processes (GPs) allow for high flexibility and low computational cost when performing approximate inference in GP models. They are particularly suitable for modeling data exhibiting global structure but are limited to stationary covariance functions and thus fail to model non-stationary data effectively. We propose Inter-domain Deep Gaussian Processes, an extension of inter-domain shallow GPs that combines the advantages of inter-domain and deep Gaussian processes (DGPs)...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0001-7833-1983
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Mansfield College
Role:
Author
ORCID:
0000-0001-5547-9213
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0002-2733-2078
Publisher:
Proceedings of Machine Learning Research
Volume:
119
Publication date:
2020-11-21
Acceptance date:
2020-06-01
Event title:
37th International Conference on Machine Learning (ICML 2020)
Event location:
Vienna, Austria
Language:
English
Keywords:
Pubs id:
1125287
Local pid:
pubs:1125287
Deposit date:
2020-08-12

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