ReLax: Efficient and Scalable Recourse Explanation Benchmarking using JAX

Python Jupyter Notebook SCSS Submitted 24 January 2024Published 12 November 2024
Review

Editor: @Fei-Tao (all papers)
Reviewers: @GarrettMerz (all reviews), @duhd1993 (all reviews)

Authors

Hangzhi Guo (0009-0000-6277-9003), Xinchang Xiong, Wenbo Zhang, Amulya Yadav (0009-0005-4638-9140)

Citation

Guo et al., (2024). ReLax: Efficient and Scalable Recourse Explanation Benchmarking using JAX. Journal of Open Source Software, 9(103), 6567, https://doi.org/10.21105/joss.06567

@article{Guo2024, doi = {10.21105/joss.06567}, url = {https://doi.org/10.21105/joss.06567}, year = {2024}, publisher = {The Open Journal}, volume = {9}, number = {103}, pages = {6567}, author = {Hangzhi Guo and Xinchang Xiong and Wenbo Zhang and Amulya Yadav}, title = {ReLax: Efficient and Scalable Recourse Explanation Benchmarking using JAX}, journal = {Journal of Open Source Software} }
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JAX machine learning interpretability counterfactual explanation recourse

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