Data valuation pertains to assessing and quantifying the value or impact of individual data points within a training dataset. This assessment is crucial, as not all data contribute equally to the learning process or the performance of a machine learning model. Some data points may carry a wealth of information, significantly enhancing model accuracy or aiding in generalization. Conversely, others may contribute little to no useful information, or worse, negatively impact model performance due to noise, outliers, or biases. In the context of machine learning, data valuation methods serve several key functions. Firstly, they allow for the identification of the most informative and influential data points, which can be leveraged to optimize the training process. This is particularly useful in scenarios where data acquisition or processing is costly, enabling a more efficient use of resources. Secondly, data valuation techniques can aid in diagnosing and improving model robustness by identifying and mitigating the impact of harmful or misleading data. Finally, in applications like active learning, data valuation can guide the selection of data points most likely to improve model performance when labeled, thus streamlining the learning process.