Official pytorch implementation of "Learning fair representation with a parametric integral probability metric" published in ICML 2022 by Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, and Yongdai Kim.
- python 3.6+
- torch 1.11.0+
- CUDA 10.2+
- numpy 1.22.2+
- sklearn 1.1.0+
- argparse 1.1+
- yaml 6.0+
Automatically, those environmental dependencies are installed by running the following command:
pip install -r requirements.txt
Moreover, please make sure whether the CUDA environment is available. This implementation is constructed over the GPU computing.
Practitioners can freely use other custom datasets.
- For unsupervised LFR:
python main.py --dataset adult --lmda 0.0 --lmdaR 1.0 --lmdaF 5.0 --head_net 1smooth
- For supervised LFR:
python main.py --dataset compas --lmda 1.0 --lmdaR 0.0 --lmdaF 0.1 --head_net 1smooth
- run
./execute.bash
for Adult dataset.
- The selected models and corresponding results are saved in folders
/models
and/results
.
@InProceedings{kim2022sipmlfr,
title = {Learning fair representation with a parametric integral probability metric},
author = {Kim, Dongha and Kim, Kunwoong and Kong, Insung and Ohn, Ilsang and Kim, Yongdai},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
year = {2022}
}