Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits
Abstract
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.
Cite
@article{arxiv.2402.08209,
title = {Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits},
author = {Hiroyuki Namba and Shota Horiguchi and Masaki Hamamoto and Masashi Egi},
journal= {arXiv preprint arXiv:2402.08209},
year = {2024}
}