DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
Machine Learning
2025-05-30 v1 Mathematical Software
Numerical Analysis
Numerical Analysis
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.
Cite
@article{arxiv.2505.23565,
title = {DRO: A Python Library for Distributionally Robust Optimization in Machine Learning},
author = {Jiashuo Liu and Tianyu Wang and Henry Lam and Hongseok Namkoong and Jose Blanchet},
journal= {arXiv preprint arXiv:2505.23565},
year = {2025}
}