Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits

• URL: http://arxiv.org/abs/2402.08209v1
• Date: Tue, 13 Feb 2024 04:17:48 GMT
• Title: Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits
• Authors: Hiroyuki Namba, Shota Horiguchi, Masaki Hamamoto, Masashi Egi
• Abstract summary: 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. We propose an iterativemethod to fast identify a subset of instances with low data Shapley values by using the thresholding bandit algorithm.
• Score: 7.335578524351567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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