How to Implement Bayesian Optimization

This tutorial can be seen as more advanced successor of our developer tutorial. It provides an overview of how model-based search, and in particular Bayesian optimization, is implemented in Syne Tune, and how this code can be extended in order to fit your needs. The basic developer tutorial is a prerequisite to take full advantage of the advanced tutorial here.

We hope this information inspires you to give it a try to extend Syne Tune’s Bayesian optimization to your needs. Please do consider contributing your efforts to Syne Tune.

Note

In order to develop new methodology in Syne Tune, make sure to use an installation from source. In particular, you need to have installed the dev dependencies.

• Overview of Module Structure
• Implementing a Surrogate Model
• Implementing Components of Bayesian Optimization
• Combining a Gaussian Process Model from Components