NeurIPS 2019poster43 citations
On Robustness to Adversarial Examples and Polynomial Optimization
Pranjal Awasthi, Abhratanu Dutta, Aravindan Vijayaraghavan
Abstract
We study the design of computationally efficient algorithms with provable guarantees, that are robust to adversarial (test time) perturbations. While there has been an explosion of recent work on this topic due to its connections to test time robustness of deep networks, there is limited theoretical understanding of several basic questions like (i) when and how can one design provably robust learning algorithms? (ii) what is the price of achieving robustness to adversarial examples in a computationally efficient manner?
BibTeX
@inproceedings{NEURIPS2019_10787834,
author = {Awasthi, Pranjal and Dutta, Abhratanu and Vijayaraghavan, Aravindan},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {On Robustness to Adversarial Examples and Polynomial Optimization},
url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/107878346e1d8f8fe6af7a7a588aa807-Paper.pdf},
volume = {32},
year = {2019}
}