Distributionally Robust Safe Sample Elimination under Covariate Shift
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913577589276672 |
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| author | Hanada, Hiroyuki Aoyama, Tatsuya Akahane, Satoshi Tanaka, Tomonari Okura, Yoshito Inatsu, Yu Hashimoto, Noriaki Takeno, Shion Murayama, Taro Lee, Hanju Kojima, Shinya Takeuchi, Ichiro |
| author_facet | Hanada, Hiroyuki Aoyama, Tatsuya Akahane, Satoshi Tanaka, Tomonari Okura, Yoshito Inatsu, Yu Hashimoto, Noriaki Takeno, Shion Murayama, Taro Lee, Hanju Kojima, Shinya Takeuchi, Ichiro |
| contents | We consider a machine learning setup where one training dataset is used to train multiple models across slightly different data distributions. This occurs when customized models are needed for various deployment environments. To reduce storage and training costs, we propose the DRSSS method, which combines distributionally robust (DR) optimization and safe sample screening (SSS). The key benefit of this method is that models trained on the reduced dataset will perform the same as those trained on the full dataset for all possible different environments. In this paper, we focus on covariate shift as a type of data distribution change and demonstrate the effectiveness of our method through experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05964 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Distributionally Robust Safe Sample Elimination under Covariate Shift Hanada, Hiroyuki Aoyama, Tatsuya Akahane, Satoshi Tanaka, Tomonari Okura, Yoshito Inatsu, Yu Hashimoto, Noriaki Takeno, Shion Murayama, Taro Lee, Hanju Kojima, Shinya Takeuchi, Ichiro Machine Learning We consider a machine learning setup where one training dataset is used to train multiple models across slightly different data distributions. This occurs when customized models are needed for various deployment environments. To reduce storage and training costs, we propose the DRSSS method, which combines distributionally robust (DR) optimization and safe sample screening (SSS). The key benefit of this method is that models trained on the reduced dataset will perform the same as those trained on the full dataset for all possible different environments. In this paper, we focus on covariate shift as a type of data distribution change and demonstrate the effectiveness of our method through experiments. |
| title | Distributionally Robust Safe Sample Elimination under Covariate Shift |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.05964 |