BOSH:Bayesian Optimization by Sampling Hierarchically

Moss, Henry B. and Leslie, David S. and Rayson, Paul (2020) BOSH:Bayesian Optimization by Sampling Hierarchically. In: Workshop on Real World Experiment Design and Active Learning at ICML 2020, 2020-07-132020-07-18.

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Deployments of Bayesian Optimization (BO) for functions with stochastic evaluations, such as parameter tuning via cross validation and simulation optimization, typically optimize an average of a fixed set of noisy realizations of the objective function. However, disregarding the true objective function in this manner finds a high-precision optimum of the wrong function. To solve this problem, we propose Bayesian Optimization by Sampling Hierarchically (BOSH), a novel BO routine pairing a hierarchical Gaussian process with an information-theoretic framework to generate a growing pool of realizations as the optimization progresses. We demonstrate that BOSH provides more efficient and higher-precision optimization than standard BO across synthetic benchmarks, simulation optimization, reinforcement learning and hyper-parameter tuning tasks.

Item Type:
Contribution to Conference (Paper)
Journal or Publication Title:
Workshop on Real World Experiment Design and Active Learning at ICML 2020
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Deposited On:
06 Jul 2020 08:55
Last Modified:
22 Nov 2022 14:46