BOSH: Bayesian Optimization by Sampling Hierarchically

• URL: http://arxiv.org/abs/2007.00939v1
• Date: Thu, 2 Jul 2020 07:35:49 GMT
• Title: BOSH: Bayesian Optimization by Sampling Hierarchically
• Authors: Henry B. Moss, David S. Leslie, Paul Rayson
• Abstract summary: We propose a novel BO routine pairing a hierarchical Gaussian process with an information-theoretic framework to generate a growing pool of realizations. 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.
• Score: 10.10241176664951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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