Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916123319992320 |
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| author | Namba, Hiroyuki Horiguchi, Shota Hamamoto, Masaki Egi, Masashi |
| author_facet | Namba, Hiroyuki Horiguchi, Shota Hamamoto, Masaki Egi, Masashi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08209 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits Namba, Hiroyuki Horiguchi, Shota Hamamoto, Masaki Egi, Masashi Machine Learning Artificial Intelligence 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. |
| title | Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.08209 |