Differentially Private Sliced Wasserstein Distance
This repository contains the code of our paper "Differentially Private Sliced Wasserstein Distance" to appear at ICML 2021.
Installation
- Pytorch 1.8
- autodp
Short Summary
DP-SWD computation is in file distrib_distance.py ClassDANN and ClassSWD contains the DP-DANN and the DP-SWD train/inference algorithm da_settings.py and da_models are utility files that contain model and learning parameters.
the file da_dp_analysis.py presents how we have computed the noise standard deviation given a desired accuracy. For this, we use the autodp package https://github.com/yuxiangw/autodp
Results reproduction
for reproducing the Domain adaptation results, one has to run the da_digits.py file. Selecting the settings allows to train either MNIST-USPS or USPS-MNIST.
Citation
if you use this code for your research, please cite our work
@InProceedings{pmlr-v139-rakotomamonjy21a,
title = {Differentially Private Sliced Wasserstein Distance},
author = {Rakotomamonjy, Alain and Ralaivola, Liva},
booktitle = {Proceedings of the 38th International Conference on Machine Learning},
pages = {8810--8820},
year = {2021},
editor = {Meila, Marina and Zhang, Tong},
volume = {139},
series = {Proceedings of Machine Learning Research},
month = {18--24 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v139/rakotomamonjy21a/rakotomamonjy21a.pdf},
url = {https://proceedings.mlr.press/v139/rakotomamonjy21a.html}
}