This is the companion code for the benchmarking study reported in the paper Transfer Learning with Gaussian Processes for Bayesian Optimization by Petru Tighineanu et al. The paper can be found at https://arxiv.org/abs/2111.11223 (will be replaced with the AISTATS link once available) and was accepted for publication at AISTATS 2022. The code allows the users to reproduce and extend the results reported in the study. 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.

The code was tested with Python 3.8. Install inside your virtual environment as

pip install .

A Bayesian optimization experiment with a configuration specified inside transfergpbo/parameters.py can be run via

python transfergpbo/experiment.py

TransferGPBO is open-sourced under the AGPL-3.0 license. See the LICENSE file for details.

For a list of other open source components included in TransferGPBO, see the file 3rd-party-licenses.txt.