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Bayesian Optimization for Policy Search via Online-Offline Experimentation.

JOURNAL OF MACHINE LEARNING RESEARCH, no. 145 (2019)

Cited: 33|Views156

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dblp.uni-trier.de academic.microsoft.com arxiv.org

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Keywords

Bayesian optimizationmulti-task Gaussian processpolicy searchA/B testingmulti-fidelity optimization

Abstract

Online field experiments are the gold-standard way of evaluating changes to real-world interactive machine learning systems. Yet our ability to explore complex, multi-dimensional policy spaces-such as those found in recommendation and ranking problems-is often constrained by the limited number of experiments that can be run simultaneously...More

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Author

Benjamin Letham

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Eytan Bakshy

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