一个可加速机器学习鲁棒优化的开源Python库
DRO: A Python Library for Distributionally Robust Optimization in Machine Learning
- 提供14种分布鲁棒优化公式和9种基础模型
- 在大规模数据上运行速度提升10到1000倍
- 兼容scikit-learn和PyTorch，适合研究与工程应用
我们介绍dro，一个用于回归和分类任务的分布式鲁棒优化（DRO）开源Python库。该库实现了14种DRO形式和9种基础模型，支持79种不同的DRO方法。同时，dro兼容scikit-learn和PyTorch。通过向量化和优化近似技术，dro在大规模数据集上的运行时间相比基线实现降低了10倍至1000倍以上。完整文档见https://python-dro.org。
原文摘要 · Abstract (English)
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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