Constrained Bayesian Optimization with Noisy Experiments
Abstract
Randomized experiments are the gold standard for evaluating the effects of changes to real-world systems. Data in these tests may be difficult to collect and outcomes may have high variance, resulting in potentially large measurement error. Bayesian optimization is a promising technique for efficiently optimizing multiple continuous parameters, but existing approaches degrade in performance when the noise level is high, limiting its...
Paper Details
Title
Constrained Bayesian Optimization with Noisy Experiments
Published Date
Jun 1, 2019
Journal
Volume
14
Issue
2
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