DRO: A Python Library for Distributionally Robust Optimization in Machine Learning

• URL: http://arxiv.org/abs/2505.23565v1
• Date: Thu, 29 May 2025 15:39:12 GMT
• Title: DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
• Authors: Jiashuo Liu, Tianyu Wang, Henry Lam, Hongseok Namkoong, Jose Blanchet,
• Abstract summary: We introduce dro, an open-source Python library for distributionally robust optimization (DRO)<n>dro implements 14 DRO formulations and 9 backbone models, enabling 79 distinct DRO methods.<n>dro is compatible with both scikit-learn and PyTorch.
• Score: 20.33236744470794
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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