sIPM-LFR: Learning fair representation with a parametric integral probability metric

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.

Dependencies

Environments

• 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.

Available datasets

• Adult Income dataset
• COMPAS dataset
• Heritage Health dataset

Practitioners can freely use other custom datasets.

Quick start

Example commands

with a single fair hyperparameter

• 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

sweeping with many hyperparameters

• run ./execute.bash for Adult dataset.

Saved models and results

• The selected models and corresponding results are saved in folders /models and /results.

Citation

@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}
}