Gradients of the entropically smoothed and sampled WDRO objective converge to Clarke subgradients of the unregularized objective as regularization vanishes, yielding O(log N/√N) SGD convergence rates up to sampling error.
DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
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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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Unregularized limit of stochastic gradient method for Wasserstein distributionally robust optimization
Gradients of the entropically smoothed and sampled WDRO objective converge to Clarke subgradients of the unregularized objective as regularization vanishes, yielding O(log N/√N) SGD convergence rates up to sampling error.