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Gaussian processes for global optimization

Abstract:

We introduce a novel Bayesian approach to global optimization using Gaussian processes. We frame the optimization of both noisy and noiseless functions as sequential decision problems, and introduce myopic and non-myopic solutions to them. Here our solutions can be tailored to exactly the degree of confidence we require of them. The use of Gaussian processes allows us to benefit from the incorporation of prior knowledge about our objective function, and also from any derivative observations. ...

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Publication status:
Accepted
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Exeter College
Role:
Author
ORCID:
0000-0003-1959-012X
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-9305-9268
Publication date:
2009-01-18
Event title:
Third International Conference on Learning and Intelligent Optimization (LION3)
Event location:
Trento, Italy
Event website:
http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=2951&copyownerid=1896
Event start date:
2009-01-14
Event end date:
2009-01-18
Language:
English
Keywords:
Pubs id:
319004
Local pid:
pubs:319004
Deposit date:
2023-01-20

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