Practical Bayesian Optimization for Variable Cost Objectives

Abstract

We propose a novel Bayesian Optimization approach for black-box functions with an environmental variable whose value determines the tradeoff between evaluation cost and the fidelity of the evaluations. Further, we use a novel approach to sampling support points, allowing faster construction of the acquisition function. This allows us to achieve optimization with lower overheads than previous approaches and is implemented for a more general class of problem. We show this approach to be effective on synthetic and real world benchmark problems.

8 pages, 7 figures

References in corpus (2)

• Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
• Freeze-Thaw Bayesian Optimization

Cited by in corpus (13)

• A Tutorial on Bayesian Optimization
• Multi-fidelity Bayesian Optimization with Max-value Entropy Search and its parallelization
• Multi-Fidelity Bayesian Optimization via Deep Neural Networks
• Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout
• Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm
• Bayesian Optimization for Policy Search via Online-Offline Experimentation
• Deep Multi-Fidelity Active Learning of High-dimensional Outputs
• Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations
• BOSH: Bayesian Optimization by Sampling Hierarchically
• Adaptive Batching for Gaussian Process Surrogates with Application in Noisy Level Set Estimation
• Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks
• Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach
• Bayesian optimization for modular black-box systems with switching costs