ecashin / DistributionalShapley

Distributional Shapley: A Distributional Framework for Data Valuation

Data Shapley: Equitable Valuation of Data for Machine Learning

Code for implementation of "Distributional Shapley: A Distributional Framework for Data Valuation".

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

@inproceedings{ghorbani2020distributional,
  title={A Distributional Framework for Data Valuation},
  author={Ghorbani, Amirata, P. Kim, Michael and Zou, James},
  booktitle={International Conference on Machine Learning},
  year={2020}
}

Prerequisites

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

Basic Usage

To estimate an equitbale measure of value of data points coming from an underlying distribution given a machine learning model class and a performance metric (test accuracy, etc)

Authors

• Amirata Ghorbani - Website
• Michael P. Kim - Website
• James Zou - Website

License

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