COBOL: Collaborative Bayesian Optimization with Labelling Experts

This repository contains the Python code that was presented for the following paper.

[1] Wenjie Xu*, Masaki Adachi*, Colin N. Jones, Michael A. Osborne, Principled Bayesian Optimization in Collaboration with Human Experts. Advances in Neural Information Processing Systems 35 (NeurIPS; Spotlight), 2024
Links: NeurIPS proceedings, arXiv, OpenReview
*: Equal contribution

Brief explanation

BO-expert collaboration framework: The algorithm (red) decides if an expert's (blue) label is necessary. If rejected, it generates a different candidate; otherwise, it directly queries.

Tutorials for practitioners/researchers

We prepared detailed explanations about how to use COBOL for your tasks.
See tutorial.ipynb.

Installation

COBOL needs the following libraries.

pip install gpytorch botorch casadi 

Cite as

Please cite this work as

@article{xu2024principled,
  title={Principled Bayesian Optimization in Collaboration with Human Experts},
  author={Xu, Wenjie and Adachi, Masaki and Jones, Colin N and Osborne, Michael A},
  journal={https://doi.org/10.48550/arXiv.2410.10452},
  year={2024}
}