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Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper−Parameters

Abstract:

Bayesian optimisation has gained great popularity as a tool for optimising the parameters of machine learning algorithms and models. Somewhat ironically, setting up the hyper-parameters of Bayesian optimisation methods is notoriously hard. While reasonable practical solutions have been advanced, they can often fail to find the best optima. Surprisingly, there is little theoretical analysis of this crucial problem in the literature. To address this, we derive a cumulative regret bound for Baye...

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Publisher:
University of Oxford
Publication date:
2014-01-01
URN:
uuid:2f35eb0d-13dd-47ae-8bc1-6d0b64f97e91
Local pid:
cs:8818

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