Trieste
Library for Bayesian optimization and active learning
Description:
A Tensorflow library (successor to GPFlowOpt) with the latest Bayesian optimization strategies. Bayesian optimization can be used for efficient hyperparameter tuning (AutoML), smart labeling of expensive data, data-efficient engineering design, and much more.
More Information:
- Grand Challenge/Workpackage(s): GC1 - WP4
- Responsible Research Lead: Tom Dhaene
- Authors: Jixiang Qi, Nasrulloh Loka, Ivo Couckuyt
- Programming Language: Python
- Link to the website
- Link to the repository
- Categories: Optimization, Active learning
