تقرير
Distributionally Robust Safe Screening
| العنوان: | Distributionally Robust Safe Screening |
|---|---|
| المؤلفون: | Hanada, Hiroyuki, Akahane, Satoshi, Aoyama, Tatsuya, Tanaka, Tomonari, Okura, Yoshito, Inatsu, Yu, Hashimoto, Noriaki, Murayama, Taro, Hanju, Lee, Kojima, Shinya, Takeuchi, Ichiro |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.16328 |
| رقم الأكسشن: | edsarx.2404.16328 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |