Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits
This work addresses the efficiency problem in data cleansing for machine learning practitioners, offering an incremental improvement over existing methods.
The paper tackles the computational expense of Data Shapley for data cleansing by proposing an iterative method using thresholding bandits to quickly identify harmful instances, with empirical results showing improved computational speed while maintaining model performance.
Data cleansing aims to improve model performance by removing a set of harmful instances from the training dataset. Data Shapley is a common theoretically guaranteed method to evaluate the contribution of each instance to model performance; however, it requires training on all subsets of the training data, which is computationally expensive. In this paper, we propose an iterativemethod to fast identify a subset of instances with low data Shapley values by using the thresholding bandit algorithm. We provide a theoretical guarantee that the proposed method can accurately select harmful instances if a sufficiently large number of iterations is conducted. Empirical evaluation using various models and datasets demonstrated that the proposed method efficiently improved the computational speed while maintaining the model performance.