Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits
<|reference_start|>Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits: 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.<|reference_end|>
@article{namba2024thresholding,
    title={Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits},
    author={Hiroyuki Namba, Shota Horiguchi, Masaki Hamamoto, Masashi Egi},
    journal={arXiv preprint arXiv:2402.08209},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08209},
    primaryClass={cs.LG cs.AI}
}