Data Shapley: Equitable Valuation of Data for Machine Learning

2019 inproceedings ghorbani2019data ○ Not yet verified

Authors

Amirata Ghorbani, James Zou

Venue

Proceedings of the 36th International Conference on Machine Learning (ICML)

Abstract

Proposes data Shapley as a metric to quantify the value of each training datum to predictor performance, satisfying equitable data valuation properties from cooperative game theory.

BibTeX

Local Entry

@inproceedings{ghorbani2019data,
  title = {Data Shapley: Equitable Valuation of Data for Machine Learning},
  author = {Amirata Ghorbani and James Zou},
  year = {2019},
  booktitle = {Proceedings of the 36th International Conference on Machine Learning (ICML)},
  url = {https://proceedings.mlr.press/v97/ghorbani19c.html},
  abstract = {Proposes data Shapley as a metric to quantify the value of each training datum to predictor performance, satisfying equitable data valuation properties from cooperative game theory.}
}

From OPENALEX

@inproceedings{ghorbani2019data,
  title = {Data Shapley: Equitable Valuation of Data for Machine Learning},
  author = {Amirata Ghorbani and James Zou},
  year = {2019},
  booktitle = {arXiv (Cornell University)},
  doi = {10.48550/arxiv.1904.02868}
}