MetaBO - Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization
This is the source code accompanying the paper Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization by Volpp et al., ICLR 2020. The paper can be found here. The code allows to reproduce the results from the paper and to train neural acquisition functions on new problems.
Purpose of the project
This software is a research prototype, solely developed for and published as part of the publication cited above. It will neither be maintained nor monitored in any way.
Clone this repository and run
conda env create -f environment.yml conda activate metabo
to create and activate a new conda environment named "metabo" with all python packages required to run the experiments.
Run the code
- Scripts to reproduce the results presented in the paper. These scripts are named evaluate_metabo_<experiment_name>.py. They load pre-trained network weights stored in /metabo/iclr2020/<experiment_name> to reproduce the results without the need of re-training neural acquisition functions. To run these scripts, execute
- Scripts to re-train the aforementioned neural acquisition functions. These scripts are named train_metabo_<experiment_name>.py. To run these scripts, execute
"Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization" is open-sourced under the APGL-3.0 license. See the LICENSE file for details.