namkoong-lab/dro
A package of distributionally robust optimization (DRO) methods. Implemented via cvxpy and PyTorch
This package helps machine learning practitioners build more reliable predictive models, especially when real-world data might differ slightly from training data. You input your labeled datasets (features and target labels) for tasks like classification or regression. The output is a robust model that's less sensitive to unexpected variations in future data. This is for data scientists, machine learning engineers, and researchers who develop and deploy predictive models.
157 stars. Available on PyPI.
Use this if you need to develop machine learning models (like SVM, logistic, or linear regression) that are resilient to potential shifts or uncertainties in your data distribution.
Not ideal if you are looking for a general-purpose machine learning library for deep learning or complex, non-linear models outside of distributionally robust optimization.
Stars
157
Forks
10
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 08, 2026
Commits (30d)
0
Dependencies
13
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