RoBO: A Flexible and Robust Bayesian Optimization Framework in Python

Bayesian optimization is a powerful approach for the global derivative-free optimization of non-convex expensive functions. Even though there is a rich literature on Bayesian optimization, the source code of advanced methods is rarely available, making it difficult for practitioners to use them and for researchers to compare to and extend them. The BSD-licensed python package ROBO, released with this paper, tackles these problems by facilitating both ease of use and extensibility. Beyond the standard methods in Bayesian optimization, RoBO offers (to the best of our knowledge) the only available implementations of Bayesian optimization with Bayesian neural networks, multi-task optimization, and fast Bayesian hyperparameter optimization on large datasets (Fabolas).