Selecting the selector: Comparison of update rules for discrete global optimization

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dc.contributor.authorTheiler, James
dc.contributor.authorZimmer, Beate G.
dc.date.accessioned2022-02-24T14:47:13Z
dc.date.available2022-02-24T14:47:13Z
dc.date.issued2017-04-01
dc.description.abstractWe compare some well-known Bayesian global optimization methods in four distinct regimes, corresponding to high and low levels of measurement noise and to high and low levels of "quenched noise" which term we use to describe the roughness of the function we are trying to optimize. We isolate the two stages of this optimization in terms of a "regressor," which fits a model to the data measured so far, and a "selector," which identifies the next point to be measured. The focus of this paper is to investigate the choice of selector when the regressor is well matched to the data.en_US
dc.identifier.citationTheiler, J. and Zimmer, B.G., 2017. Selecting the selector: Comparison of update rules for discrete global optimization. Statistical Analysis and Data Mining: The ASA Data Science Journal, 10(4), pp.211-229.en_US
dc.identifier.doihttps://doi.org/10.1002/sam.11343
dc.identifier.urihttps://hdl.handle.net/1969.6/90194
dc.language.isoen_USen_US
dc.publisherACMen_US
dc.subjectglobal optimizationen_US
dc.subjectquenched noiseen_US
dc.subjectbayesiaen_US
dc.titleSelecting the selector: Comparison of update rules for discrete global optimizationen_US
dc.typeArticleen_US

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