Finding Mixed Nash Equilibria of Generative Adversarial Networks. - Ya-Ping Hsieh; Chen Liu 027; Volkan Cevher (ICML, 2019)

On Certifying Non-Uniform Bounds against Adversarial Attacks. - Chen Liu 027; Ryota Tomioka; Volkan Cevher (ICML, 2019)

On Certifying Non-uniform Bound against Adversarial Attacks. - Chen Liu 027; Ryota Tomioka; Volkan Cevher (CoRR, 2019)

On Certifying Robust Models by Polyhedral Envelope. - Chen Liu 027; Mathieu Salzmann; Sabine Süsstrunk (CoRR, 2019)

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them. - Chen Liu 027; Mathieu Salzmann; Tao Lin 004; Ryota Tomioka; Sabine Süsstrunk (NeurIPS, 2020)

On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training. - Chen Liu 027; Zhichao Huang 002; Mathieu Salzmann; Tong Zhang 023; Sabine Süsstrunk (CoRR, 2021)

Robust Binary Models by Pruning Randomly-initialized Networks. - Chen Liu 027; Ziqi Zhao; Sabine Süsstrunk; Mathieu Salzmann (NeurIPS, 2022)

Towards Stable and Efficient Adversarial Training against l1 Bounded Adversarial Attacks. - Yulun Jiang; Chen Liu 027; Zhichao Huang 002; Mathieu Salzmann; Sabine Süsstrunk (ICML, 2023)

Towards Mitigating Architecture Overfitting in Dataset Distillation. - Xuyang Zhong; Chen Liu 027 (CoRR, 2023)

Fast Adversarial Training With Adaptive Step Size. - Zhichao Huang 002; Yanbo Fan; Chen Liu 027; Weizhong Zhang; Yong Zhang 034; Mathieu Salzmann; Sabine Süsstrunk; Jue Wang 001 (IEEE Trans. Image Process., 2023)

Training Provably Robust Models by Polyhedral Envelope Regularization. - Chen Liu 027; Mathieu Salzmann; Sabine Süsstrunk (IEEE Trans. Neural Networks Learn. Syst., 2023)

Towards Efficient Training and Evaluation of Robust Models against l0 Bounded Adversarial Perturbations. - Xuyang Zhong; Yixiao Huang 004; Chen Liu 027 (ICML, 2024)

Towards Efficient Training and Evaluation of Robust Models against l0 Bounded Adversarial Perturbations. - Xuyang Zhong; Yixiao Huang 004; Chen Liu 027 (CoRR, 2024)

Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density. - Shuangqi Li; Chen Liu 027; Tong Zhang 023; Hieu Le 001; Sabine Süsstrunk; Mathieu Salzmann (Trans. Mach. Learn. Res., 2024)

Fast Adversarial Training against Sparse Attacks Requires Loss Smoothing. - Xuyang Zhong; Yixiao Huang 004; Chen Liu 027 (CoRR, 2025)

DualOptim: Enhancing Efficacy and Stability in Machine Unlearning with Dual Optimizers. - Xuyang Zhong; Haochen Luo; Chen Liu 027 (CoRR, 2025)