Summary of Distributionally Robust Safe Sample Elimination Under Covariate Shift, by Hiroyuki Hanada et al.
Distributionally Robust Safe Sample Elimination under Covariate Shift
by Hiroyuki Hanada, Tatsuya Aoyama, Satoshi Akahane, Tomonari Tanaka, Yoshito Okura, Yu Inatsu, Noriaki Hashimoto, Shion Takeno, Taro Murayama, Hanju Lee, Shinya Kojima, Ichiro Takeuchi
First submitted to arxiv on: 10 Jun 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 DRSSS method combines DR optimization and SSS to reduce storage and training costs in machine learning setups where customized models are needed for various deployment environments. The method is designed to train multiple models across slightly different data distributions, ensuring that models trained on a reduced dataset perform the same as those trained on the full dataset for all possible different environments. This approach is particularly effective in handling covariate shift, a type of data distribution change. Through experiments, the authors demonstrate the effectiveness of their method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re going to explore a new way to train machine learning models that need to work well in many different situations. Imagine you have one big dataset, but you want to make smaller versions for each situation where your model will be used. This can save time and money! The authors created a new method called DRSSS, which helps make these smaller datasets just as good as the bigger one. They tested it and showed that it works well when data distribution changes happen. |
Keywords
» Artificial intelligence » Machine learning » Optimization
