Purpose of this fork is to complete the pipeline for our work proposed in the paper 'Discrete randomized smoothing meets quantum computing'

Reference implementation of the certificates proposed in the paper:

"Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More"

Aleksandar Bojchevski, Johannes Klicpera, and Stephan Günnemann, ICML 2020.

The notebook demo.ipynb shows an example of how to use our binary certificate for a pretrained GCN model. You can use scripts/train_and_cert.py to train and certify a model from scratch on a cluster using SEML.

Please cite our paper if you use this code in your own work:

@inproceedings{bojchevski_sparsesmoothing_2020,
title = {Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More},
author = {Bojchevski, Aleksandar and Klicpera, Johannes and G{\"u}nnemann, Stephan},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {1003--1013},
year = {2020}
}