CoExBO

Source code for the AISTATS 2024 paper
"Looping in the Human: Collaborative and Explainable Bayesian Optimization" arXiv

DEMO for practitioners/researchers

We prepared an example of CoExBO with battery example.

• Demo1 human feedback for battery experiments.ipynb
• Demo2 synthetic human response.ipynb

CoExBO in a nutshell.

Collaborative and Explainable BO (CoExBO)

1. BO combines experimental results and expert preferences.
   
2. BO generates pairwise candidates along with explanations.
   
3. Human interprets the acquisitions and picks their preferred candidate
   
4. Human conducts experiments and repeat step 1.
   

Explainability

Utilising GP-SHAP, we can provide insights into the undergoing of the BO by attributing feature importance to the followings:

• Surrogate GP model
• Acquisition function (GP-UCB)

Dependencies

botorch 0.8.4 gpytorch 1.10 torch 1.13.0

Cite as

Please cite this work as

@inproceedings{adachi2023looping,
  title={Looping in the Human: Collaborative and Explainable Bayesian Optimization},
  author={Adachi, Masaki and Planden, Brady and Howey, David A and Maundet, Krikamol and Osborne, Michael A and Chau, Siu Lun},
  booktitle={Artificial intelligence and statistics},
  year={2024}
}