Skip to main content

Taking the Human Out of the Loop: A Review of Bayesian Optimization

Bobak Shahriari‚ Kevin Swersky‚ Ziyu Wang‚ Ryan P. Adams and Nando de Freitas


Big data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involves many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.

Universities of Harvard‚ Oxford‚ Toronto‚ and Google DeepMind