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Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy.
Theory of Reproducing Kernels
N. Aronszajn · 1950
Earlier work this paper cites.
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Erwin Kreyszig · 1978
Earlier work this paper cites.
Probability theory and elements of measure theory
H. Bauer · 1981
Earlier work this paper cites.
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Roger A. Horn and Charles R. Johnson · 1985
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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