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

Data valuation, i.e., measuring the contribution of a data source to the ML training process, is an important problem in the field of machine learning (ML). For example, assessing the value of data helps to identify and remove low-quality data [Ghorbani and Zou, 2019, Kwon and Zou, 2021], and also provides insights into a model's test-time behavior [Koh and Liang, 2017]. Additionally, data valuation plays a critical role in incentivizing data sharing and shaping policies for data market [Zhu et al., 2019, Tian et al., 2022]. Cooperative game theory and economic principles have inspired the use of the Shapley value (SV) as a principled approach for data valuation [Ghorbani and Zou, 2019, Jia et al., 2019]. The SV is the unique notion that satisfies natural fairness requirements in the ML context.