Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning

2022 inproceedings kwon2022beta ○ Not yet verified

Authors

Yongchan Kwon, James Zou

Venue

International Conference on Artificial Intelligence and Statistics (AISTATS)

Abstract

Generalizes Data Shapley using Beta weighting functions, providing noise-reduced data valuation that better handles outliers and mislabeled data detection.

BibTeX

Local Entry

@inproceedings{kwon2022beta,
  title = {Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning},
  author = {Yongchan Kwon and James Zou},
  year = {2022},
  booktitle = {International Conference on Artificial Intelligence and Statistics (AISTATS)},
  url = {https://proceedings.mlr.press/v151/kwon22a.html},
  abstract = {Generalizes Data Shapley using Beta weighting functions, providing noise-reduced data valuation that better handles outliers and mislabeled data detection.}
}

From OPENALEX

@inproceedings{kwon2022beta,
  title = {Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning},
  author = {Yongchan Kwon and James Zou},
  year = {2021},
  booktitle = {arXiv (Cornell University)},
  doi = {10.48550/arxiv.2110.14049}
}