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Learning Controllable Fair Representations

TensorFlow implementation for the paper Learning Controllable Fair Representations, AISTATS 2019.

Overview

Running the experiments

Requirements:

  • Tensorflow
  • pandas
  • numpy
  • tf_utils

How to install tf_utils

git clone git@github.com:jiamings/tf_utils.git
cd tf_utils
pip install -e .

Running MIFR (Adult)

python -m exmaples.adult

Running L-MIFR (Adult)

python -m examples.adult --lag

Options

If MIFR then the e hyperparameter values corresponds to individual lambda parameters, if L-MIFR then they correspond to epsilon contraints in the paper.

  • e1: Upper bound for MI
  • e2: Adversarial approximation to Demographic parity
  • e4: Adversarial approximation to Equalized odds
  • e5: Adversarial approximation to Equalized opportunity
  • disc: discriminator iterations

References

If you find the idea or code useful for your research, please consider citing our paper:

@article{song2019learning,
  title={Learning Controllable Fair Representations},
  author={Song, Jiaming and and Grover, Aditya and Zhao, Shengjia and Ermon, Stefano},
  journal={arXiv preprint arXiv:1812.04218},
  year={2018}
}

Acknowledgements

utils/logger.py is based on an implementation in OpenAI Baselines.

Contact

tsong [at] cs [dot] stanford [dot] edu

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