Sampling Acquisition Functions for Batch Bayesian Optimization
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian OptimizationThompson SamplingSimilar Papers 제목 키워드 기반
Batched Energy-Entropy acquisition for Bayesian Optimization
Bayesian optimization (BO) is an attractive machine learning framework for performing sample-efficient global optimization of black-box functions. The optimization process is guided by an acquisition function that select…
Bayesian OptimizationGaussian Processesglobal-optimizationBatch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
Bayesian optimization methods are promising for the optimization of black-box functions that are expensive to evaluate. In this paper, a novel batch Bayesian optimization approach is proposed. The parallelization is…
Bayesian OptimizationBudgeted Batch Bayesian Optimization With Unknown Batch Sizes
Parameter settings profoundly impact the performance of machine learning algorithms and laboratory experiments. The classical grid search or trial-error methods are exponentially expensive in large parameter spaces, and …
Bayesian OptimizationBIG-bench Machine LearningExperimental Designglobal-optimizationBatched Bayesian optimization by maximizing the probability of including the optimum
Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategies for batch design in BO aim to balance …
Bayesian OptimizationDiversityThompson SamplingOptimal Initialization of Batch Bayesian Optimization
Field experiments and computer simulations are effective but time-consuming methods of measuring the quality of engineered systems at different settings. To reduce the total time required, experimenters may employ Bayesi…
Bayesian Optimization