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The Shapley value (SV) has emerged as a promising method for data valuation.However, computing or estimating the SV is often computationally expensive. Toovercome this challenge, Jia et al. (2019) propose an advanced SV estimationalgorithm called ``Group Testing-based SV estimator'' which achieves favorableasymptotic sample complexity. In this technical note, we present severalimprovements in the analysis and design choices of this SV estimator. Moreover,we point out that the Group Testing-based SV estimator does not fully reuse thecollected samples. Our analysis and insights contribute to a betterunderstanding of the challenges in developing efficient SV estimationalgorithms for data valuation.

A Note on "Towards Efficient Data Valuation Based on the Shapley Value''