  The Shapley value (SV) has emerged as a promising method for data valuation.
However, computing or estimating the SV is often computationally expensive. To
overcome this challenge, Jia et al. (2019) propose an advanced SV estimation
algorithm called ``Group Testing-based SV estimator'' which achieves favorable
asymptotic sample complexity. In this technical note, we present several
improvements 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 the
collected samples. Our analysis and insights contribute to a better
understanding of the challenges in developing efficient SV estimation
algorithms for data valuation.
