https://github.com/birkhoffg/rocoursenet
This is the official repository of the paper "RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model".
Science Score: 36.0%
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Keywords
Repository
This is the official repository of the paper "RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model".
Basic Info
- Host: GitHub
- Owner: BirkhoffG
- License: apache-2.0
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://arxiv.org/abs/2206.00700
- Size: 83.6 MB
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Metadata Files
README.md
RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model
This repo contains code to reproduce our paper published at CIKM 2023.
To cite this paper:
bibtex
@inproceedings{guo2023rocoursenet,
author = {Guo, Hangzhi and Jia, Feiran and Chen, Jinghui and Squicciarini, Anna and Yadav, Amulya},
title = {RoCourseNet: Robust Training of a Prediction Aware Recourse Model},
year = {2023},
isbn = {9798400701245},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3583780.3615040},
doi = {10.1145/3583780.3615040},
booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
pages = {619–628},
numpages = {10},
keywords = {explainable artificial intelligence, adversarial machine learning, counterfactual explanation, algorithmic recourse, interpretability},
location = {Birmingham, United Kingdom},
series = {CIKM '23}
}
Install
This project uses jax-relax (a fast and scalable recourse explanation library). Ths library is highly scalable and extensible, which enables our experiments to be finished within 30 minutes. In contrast, a pytorch implementation of RoCourseNet takes around 12 hours to run.
sh
pip install -e ".[dev]" --upgrade
Run Experiments
Running scripts.experiment.py with different arguments will reproduce results in our paper. For example,
- Train and Evaluate RoCourseNet on Loan Application Dataset:
sh
python -m scripts.experiment.py -d loan
- Train and Evaluate CounterNet on Loan Application Dataset:
sh
python -m scripts.experiment.py -m CounterNet -d loan
- Train and Evaluate ROAR on Loan Application Dataset:
sh
python -m scripts.experiment.py -m ROAR -d loan
Owner
- Name: Hangzhi Guo
- Login: BirkhoffG
- Kind: user
- Company: Penn State University
- Website: https://birkhoffg.github.io
- Twitter: BirkhoffGuo
- Repositories: 4
- Profile: https://github.com/BirkhoffG
Ph.D. Student at Penn State University
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