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

Abstract:We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 15 DRO formulations and 9 backbone models, enabling 85 distinct DRO methods. Furthermore, dro is compatible with both scikit-learn and PyTorch through a unified interface, and uses approximation techniques to reduce runtime for some computationally demanding formulations. Comprehensive documentation is available at this https URL.

Machine Learning (cs.LG); Mathematical Software (cs.MS); Numerical Analysis (math.NA)

arXiv:2505.23565 [cs.LG]

(or arXiv:2505.23565v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2505.23565 Focus to learn more arXiv-issued DOI via DataCite

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