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arXiv · 2404.16328

Distributionally Robust Safe Screening

Hiroyuki Hanada·Satoshi Akahane·Tatsuya Aoyama·Tomonari Tanaka·Yoshito Okura·Yu Inatsu·Noriaki Hashimoto·Taro Murayama·Lee Hanju·Shinya Kojima·Ichiro Takeuchi
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Abstract

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.

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Hiroyuki Hanada, Satoshi Akahane, Tatsuya Aoyama, Tomonari Tanaka, Yoshito Okura, Yu Inatsu, Noriaki Hashimoto, Taro Murayama, Lee Hanju, Shinya Kojima, Ichiro Takeuchi. 2024-04-25. Distributionally Robust Safe Screening. https://arxiv.org/abs/2404.16328

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