Differentiable Abstract Interpretation for Provably Robust Neural Networks

@InProceedings{pmlr-v80-mirman18b,
  title = 	 {Differentiable Abstract Interpretation for Provably Robust Neural Networks},
  author =       {Mirman, Matthew and Gehr, Timon and Vechev, Martin},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {3578--3586},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--15 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v80/mirman18b/mirman18b.pdf},
  url = 	 {https://proceedings.mlr.press/v80/mirman18b.html},
  abstract = 	 {We introduce a scalable method for training robust neural networks based on abstract interpretation. We present several abstract transformers which balance efficiency with precision and show these can be used to train large neural networks that are certifiably robust to adversarial perturbations.}
}