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

Understand

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.