This repository contains the supporting code for the paper:

Matthew Staib, Stefanie Jegelka. Distributionally Robust Optimization and Generalization in Kernel Methods. In Advances in Neural Information Processing Systems 32, 2019.

@inproceedings{staib2019distributionally,
 author = {Staib, Matthew and Jegelka, Stefanie},
 title = {Distributionally Robust Optimization and Generalization in Kernel Methods},
 booktitle = {Advances in Neural Information Processing Systems 32},
 year = {2019}
}

The only language used was Python 3 with a virtualenv -- all the installed packages in the virtualenv are listed in list_of_installed_packages.txt (Most of these are probably unnecessary except for scikit-learn and numpy/scipy.)

All you need to do is load up a virtualenv with all the packages listed above, and then run the two included notebooks.