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Data Shapley: Equitable Valuation of Data for Machine Learning

Code for implementation of "Data Shapley: Equitable Valuation of Data for Machine Learning".

Please cite the following work if you use this benchmark or the provided tools or implementations:

@inproceedings{ghorbani2019data,
  title={Data Shapley: Equitable Valuation of Data for Machine Learning},
  author={Ghorbani, Amirata and Zou, James},
  booktitle={International Conference on Machine Learning},
  pages={2242--2251},
  year={2019}
}

Prerequisites

• Python, NumPy, Tensorflow 1.12, Scikit-learn, Matplotlib

Basic Usage

To divide value fairly between individual train data points/sources given the learning algorithm and a meausre of performance for the trained model (test accuracy, etc)

Authors

• Amirata Ghorbani - Website
• James Zou - Website

License

This project is licensed under the MIT License - see the LICENSE.md file for details