Simple and Scalable Parallelized Bayesian Optimization
This work addresses the need for scalable asynchronous parallel Bayesian optimization for researchers and practitioners dealing with expensive-to-evaluate problems like hyperparameter tuning, though it appears incremental as it builds on existing parallel BO methods.
The authors tackled the problem of inefficient parallel Bayesian optimization in asynchronous settings by proposing a simple and scalable method, which demonstrated promising performance in experiments on benchmark functions and hyperparameter optimization of multi-layer perceptrons.
In recent years, leveraging parallel and distributed computational resources has become essential to solve problems of high computational cost. Bayesian optimization (BO) has shown attractive results in those expensive-to-evaluate problems such as hyperparameter optimization of machine learning algorithms. While many parallel BO methods have been developed to search efficiently utilizing these computational resources, these methods assumed synchronous settings or were not scalable. In this paper, we propose a simple and scalable BO method for asynchronous parallel settings. Experiments are carried out with a benchmark function and hyperparameter optimization of multi-layer perceptrons, which demonstrate the promising performance of the proposed method.