no code implementations • ICML Workshop AML 2021 • Theodoros Tsiligkaridis, Jay Roberts
We develop a theoretical framework for adversarial training (AT) with FW optimization (FW-AT) that reveals a geometric connection between the loss landscape and the distortion of $\ell_\infty$ FW attacks (the attack's $\ell_2$ norm).
1 code implementation • CVPR 2022 • Theodoros Tsiligkaridis, Jay Roberts
We develop a theoretical framework for adversarial training with FW optimization (FW-AT) that reveals a geometric connection between the loss landscape and the $\ell_2$ distortion of $\ell_\infty$ FW attacks.
no code implementations • 30 Nov 2020 • Jay Roberts, Theodoros Tsiligkaridis
Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic.
no code implementations • 10 Sep 2020 • Theodoros Tsiligkaridis, Jay Roberts
It is shown that using only a single iteration in our regularizer achieves stronger robustness than prior gradient and curvature regularization schemes, avoids gradient obfuscation, and, with additional iterations, achieves strong robustness with significantly lower training time than AT.