9 citations · 9 across the 1 of their papers we have counts for
3 papers
cs.LG2020
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
Chen Liu, Mathieu Salzmann, Tao Lin +2
We analyze the influence of adversarial training on the loss landscape of machine learning models. To this end, we first provide analytical studies of the properties of adversarial…
cs.LG2019★ 9 cited
On Certifying Non-uniform Bound against Adversarial Attacks
Chen Liu, Ryota Tomioka, Volkan Cevher
This work studies the robustness certification problem of neural network models, which aims to find certified adversary-free regions as large as possible around data points. In con…
cs.LG2018
Finding Mixed Nash Equilibria of Generative Adversarial Networks
Ya-Ping Hsieh, Chen Liu, Volkan Cevher
We reconsider the training objective of Generative Adversarial Networks (GANs) from the mixed Nash Equilibria (NE) perspective. Inspired by the classical prox methods, we develop a…