Bayesian Optimization for Accelerating Hyper-Parameter Tuning

Abstract

Bayesian optimization (BO) has recently emerged as a powerful and flexible tool for hyper-parameter tuning and more generally for the efficient global optimization of expensive black box functions. Systems implementing BO has successfully solved difficult problems in automatic design choices and machine learning hyper-parameters tuning. Many recent advances in the methodologies and theories underlying Bayesian optimization have extended the framework to new applications and provided greater insights into the behavior of these algorithms. In this paper, we summarize our recent research in Bayesian optimization, highlight our contribution and present future research directions.