Sampling Acquisition Functions for Batch Bayesian Optimization

2019/03/22 by Alessandro De Palma, De Palma, Alessandro, Celestine Mendler-Dünner +7
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification

paper · pdf · doi:10.48550/arxiv.1903.09434

openalex publication_date 2019/03/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present Acquisition Thompson Sampling (ATS), a novel technique for batch Bayesian Optimization (BO) based on the idea of sampling multiple acquisition functions from a stochastic process. We define this process through the dependency of the acquisition functions on a set of model hyper-parameters. ATS is conceptually simple, straightforward to implement and, unlike other batch BO methods, it can be employed to parallelize any sequential acquisition function or to make existing parallel methods scale further. We present experiments on a variety of benchmark functions and on the hyper-parameter optimization of a popular gradient boosting tree algorithm. These demonstrate the advantages of ATS with respect to classical parallel Thompson Sampling for BO, its competitiveness with two state-of-the-art batch BO methods, and its effectiveness if applied to existing parallel BO algorithms.

Citations

• Practical Bayesian Optimization of Machine Learning Algorithms
• Predictive Entropy Search for Bayesian Optimization with Unknown Constraints
• Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process\n Bandit Optimization
• Parallel Predictive Entropy Search for Batch Global Optimization of\n Expensive Objective Functions
• Taking the Human Out of the Loop: A Review of Bayesian Optimization
• Batch Bayesian Optimization via Local Penalization

Related

• TS-RSR: A provably efficient approach for batch Bayesian Optimization
• Fast, Precise Thompson Sampling for Bayesian Optimization
• Efficient and Scalable Batch Bayesian Optimization Using K-Means
• A Less Uncertain Sampling-Based Method of Batch Bayesian Optimization
• Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes