DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
This provides a practical tool for researchers and practitioners in machine learning to apply DRO methods more efficiently, though it is incremental as it focuses on software implementation rather than new algorithmic breakthroughs.
The authors tackled the problem of implementing distributionally robust optimization (DRO) in machine learning by introducing an open-source Python library called dro, which reduces runtime by 10x to over 1000x compared to baseline implementations on large-scale datasets.
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.