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Poster
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Jian Wu · Peter Frazier

Tue Dec 06 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #146 #None

In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural network architectures in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm --- the parallel knowledge gradient method. By construction, this method provides the one-step Bayes optimal batch of points to sample. We provide an efficient strategy for computing this Bayes-optimal batch of points, and we demonstrate that the parallel knowledge gradient method finds global optima significantly faster than previous batch Bayesian optimization algorithms on both synthetic test functions and when tuning hyperparameters of practical machine learning algorithms, especially when function evaluations are noisy.

Author Information

Jian Wu (Cornell University)
Peter Frazier (Princeton University)

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