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arXiv · 2402.08209

Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits

Hiroyuki Namba·Shota Horiguchi·Masaki Hamamoto·Masashi Egi
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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.

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Hiroyuki Namba, Shota Horiguchi, Masaki Hamamoto, Masashi Egi. 2024-02-13. Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits. https://arxiv.org/abs/2402.08209

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