Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning. (arXiv:2110.14049v2 [cs.LG] UPDATED)

Data Shapley has recently been proposed as a principled framework to quantify
the contribution of individual datum in machine learning. It can effectively
identify helpful or harmful data points for a learning algorithm. In this
paper, we propose Beta Shapley, which is a substantial generalization of Data
Shapley. Beta Shapley arises naturally by relaxing the efficiency axiom of the
Shapley value, which is not critical for machine learning settings. Beta
Shapley unifies several popular data valuation methods and includes data …

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