Metadata-Version: 2.1
Name: dro
Version: 0.0.1
Summary: A package of distributionally robust optimization (DRO) methods. Implemented via cvxpy and PyTorch
Home-page: https://github.com/namkoong-lab/dro
Author: Jiashuo Liu, Tianyu Wang, Peng Cui, Hongseok Namkoong
Author-email: liujiashuo77@gmail.com, tw2837@columbia.edu, cuip@tsinghua.edu.cn, namkoong@gsb.columbia.edu
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: scikit-learn
Requires-Dist: torch
Requires-Dist: scipy
Requires-Dist: cvxpy

### DRO Package

> <a href="https://ljsthu.github.io">Jiashuo Liu*</a>, <a href="https://wangtianyu61.github.io">Tianyu Wang*</a>, <a href="https://pengcui.thumedialab.com">Peng Cui</a>, <a href="https://hsnamkoong.github.io">Hongseok Namkoong</a>

> Tsinghua University, Columbia University


`DRO` is a python package that implements 12 typical DRO methods on linear models (SVM, logistic regression, and linear regression). It is built based on `cvxpy`. Implemented DRO methods include:
* $f$-DRO
    * CVaR-DRO
    * KL-DRO
    * TV-DRO
    * Marginal DRO (CVaR)
* Wasserstein DRO
    * Wasserstein DRO
    * Augmented Wasserstein DRO
    * Regularized Wasserstein DRO
* MMD-DRO
* Sinkhorn-DRO
* Holistic DRO
* Unified-DRO
    * $L_2$ cost
    * $L_{inf}$ cost

Current version only contains linear models. And further version will incorporate neural network implementations via some approximations.
