DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915312651206656 |
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| author | Liu, Jiashuo Wang, Tianyu Lam, Henry Namkoong, Hongseok Blanchet, Jose |
| author_facet | Liu, Jiashuo Wang, Tianyu Lam, Henry Namkoong, Hongseok Blanchet, Jose |
| contents | We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 14 DRO formulations and 9 backbone models, enabling 79 distinct DRO methods. Furthermore, dro is compatible with both scikit-learn and PyTorch. Through vectorization and optimization approximation techniques, dro reduces runtime by 10x to over 1000x compared to baseline implementations on large-scale datasets. Comprehensive documentation is available at https://python-dro.org. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23565 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DRO: A Python Library for Distributionally Robust Optimization in Machine Learning Liu, Jiashuo Wang, Tianyu Lam, Henry Namkoong, Hongseok Blanchet, Jose Machine Learning Mathematical Software Numerical Analysis We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 14 DRO formulations and 9 backbone models, enabling 79 distinct DRO methods. Furthermore, dro is compatible with both scikit-learn and PyTorch. Through vectorization and optimization approximation techniques, dro reduces runtime by 10x to over 1000x compared to baseline implementations on large-scale datasets. Comprehensive documentation is available at https://python-dro.org. |
| title | DRO: A Python Library for Distributionally Robust Optimization in Machine Learning |
| topic | Machine Learning Mathematical Software Numerical Analysis |
| url | https://arxiv.org/abs/2505.23565 |