Code for Learning-augmented private algorithms for multiple quantile release, to appear in ICML 2023.
The scripts static.py
, pubpri.py
, and online.py
are for the experiments in Sections 3.3, 5.1, and 5.2, respectively.
Note some scripts may download potentially large datasets, and the file citibike/worldnews/comments.pkl.zip
needs to be unzipped before running online.py
.
The Dockerfile
describes the Python environment used.
@inproceedings{khodak2023learning,
author={Mikhail Khodak and Kareem Amin and Travis Dick and Sergei Vassilvitskii},
title={Learning-Augmented Private Algorithms for Multiple Quantile Release},
booktitle={Proceedings of the 40th International Conference on Machine Learning},
year={2023}
}