Practical Bayesian Optimization for Variable Cost Objectives

2017/03/13 by Mark McLeod, McLeod, Mark, Michael A. Osborne +3 · 1 citation
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1703.04335

openalex publication_date 2017/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

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

Cited by

• A Tutorial on Bayesian Optimization

Related

• Understanding an Acquisition Function Family for Bayesian Optimization
• Bayesian Optimization of Risk Measures
• Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics
• Differentiating Policies for Non-Myopic Bayesian Optimization
• Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs