| Autor: |
Hanada, Hiroyuki, Akahane, Satoshi, Aoyama, Tatsuya, Tanaka, Tomonari, Okura, Yoshito, Inatsu, Yu, Hashimoto, Noriaki, Murayama, Taro, Hanju, Lee, Kojima, Shinya, Takeuchi, Ichiro |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method effectively combines DR learning, a paradigm aimed at enhancing model robustness against variations in data distribution, with safe screening (SS), a sparse optimization technique designed to identify irrelevant samples and features prior to model training. The core concept of the DRSS method involves reformulating the DR covariate-shift problem as a weighted empirical risk minimization problem, where the weights are subject to uncertainty within a predetermined range. By extending the SS technique to accommodate this weight uncertainty, the DRSS method is capable of reliably identifying unnecessary samples and features under any future distribution within a specified range. We provide a theoretical guarantee of the DRSS method and validate its performance through numerical experiments on both synthetic and real-world datasets. |
| Databáze: |
arXiv |
| Externí odkaz: |
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