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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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Neural Information Processing Systems
2022
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| Online Access: | https://hdl.handle.net/1721.1/137709.2 |