Summary of Distributionally Robust Safe Screening, by Hiroyuki Hanada et al.
Distributionally Robust Safe Screening
by Hiroyuki Hanada, Satoshi Akahane, Tatsuya Aoyama, Tomonari Tanaka, Yoshito Okura, Yu Inatsu, Noriaki Hashimoto, Taro Murayama, Lee Hanju, Shinya Kojima, Ichiro Takeuchi
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Distributionally Robust Safe Screening (DRSS) method identifies unnecessary samples and features in a data distribution shift setting by combining robust learning with sparse optimization. The DRSS method reformulates the problem as a weighted empirical risk minimization, where weights are subject to uncertainty within a predetermined range. This approach extends safe screening techniques to accommodate weight uncertainty, allowing reliable identification of irrelevant samples and features under any future distribution within the specified range. Theoretical guarantees are provided, and performance is validated on synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists developed a new way to find unnecessary data points and features in a situation where the data distribution changes. They combined two techniques: robust learning to make models more resistant to changes in data distribution, and sparse optimization to remove irrelevant information. The new method reformulates the problem by considering different weights for the data points, which helps it identify unnecessary data points and features that can be removed before training a model. This approach provides a guarantee of its performance and is tested on both fake and real-world datasets. |
Keywords
» Artificial intelligence » Optimization
