From the ICML 2023 paper GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations by Dan Ley, Saumitra Mishra, and Daniele Magazzeni.
Given recent requests, we have pushed the datasets, models and classes used in our experiments. The globe_ce.py file contains the main implementation of our method.
An implementation of the AReS method is also provided, including our enhancements as detailed in Appendix C.
Any questions regarding the method or implementation can be directed to Dan Ley (email address provided in the paper). Feel free also to create an issue or pull request.
Please cite our paper if you find it useful in your research:
@inproceedings{ley2023globece,
title={GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations},
author={Dan Ley and Saumitra Mishra and Daniele Magazzeni},
booktitle={International Conference on Machine Learning},
year={2023}
}