RoBO: A Flexible and Robust Bayesian Optimization Framework in Python

NIPS 2017 2017 Aaron KleinStefan FalknerNumair MansurFrank Hutter

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... (read more)

PDF

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet