This is the companion code for the paper Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization by Lukas P. Fröhlich et al., AISTATS 2020. The paper can be found here. The code allows users to experiment with the provided acquisition function. Please cite the above paper when reporting, reproducing or extending the results.
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.
In the root directory of the repository execture the following commands:
conda env create --file=environment.yaml
conda activate nes
pip install -e .To run a comparison of different acquisition functions on the synthetic benchmark functions, execute the following:
python run_experiments.pyThis automatically creates a sub-directory in the Results/ directory. To visualize the results, adapt the path in plot_results.py and execute it via
python plot_results.pyNoisy-Input Entropy Search is open-sourced under the AGPL-3.0 license. See the LICENSE file for details.