Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
© 2018 Curran Associates Inc.All rights reserved. Bayesian optimization usually assumes that a Bayesian prior is given. However, the strong theoretical guarantees in Bayesian optimization are often regrettably compromised in practice because of unknown parameters in the prior. In this paper, we adop...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Neural Information Processing Systems,
2022-03-21T14:48:03Z.
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| Subjects: | |
| Online Access: | Get fulltext |