Chen Liu

According to our database, Chen Liu authored at least 18 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Sparse-PGD: A Unified Framework for Sparse Adversarial Perturbations Generation.

Xuyang Zhong, Chen Liu

IEEE Trans. Pattern Anal. Mach. Intell., March, 2026

2025

Toward Mitigating Architecture Overfitting on Distilled Datasets.

Xuyang Zhong, Chen Liu

IEEE Trans. Neural Networks Learn. Syst., August, 2025

Fast Adversarial Training against Sparse Attacks Requires Loss Smoothing.

Xuyang Zhong, Yixiao Huang, Chen Liu

CoRR, February, 2025

DualOptim: Enhancing Efficacy and Stability in Machine Unlearning with Dual Optimizers.

Xuyang Zhong, Haochen Luo, Chen Liu

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

2024

Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density.

Shuangqi Li, Chen Liu, Tong Zhang, Hieu Le, Sabine Süsstrunk, Mathieu Salzmann

Trans. Mach. Learn. Res., 2024

On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training.

Chen Liu, Zhichao Huang, Mathieu Salzmann, Tong Zhang, Sabine Süsstrunk

J. Mach. Learn. Res., 2024

Towards Efficient Training and Evaluation of Robust Models against l₀ Bounded Adversarial Perturbations.

Xuyang Zhong, Yixiao Huang, Chen Liu

CoRR, 2024

Towards Efficient Training and Evaluation of Robust Models against l0 Bounded Adversarial Perturbations.

Xuyang Zhong, Yixiao Huang, Chen Liu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Training Provably Robust Models by Polyhedral Envelope Regularization.

Chen Liu, Mathieu Salzmann, Sabine Süsstrunk

IEEE Trans. Neural Networks Learn. Syst., June, 2023

Fast Adversarial Training With Adaptive Step Size.

Zhichao Huang, Yanbo Fan, Chen Liu, Weizhong Zhang, Yong Zhang, Mathieu Salzmann, Sabine Süsstrunk, Jue Wang

IEEE Trans. Image Process., 2023

Towards Mitigating Architecture Overfitting in Dataset Distillation.

Xuyang Zhong, Chen Liu

CoRR, 2023

Towards Stable and Efficient Adversarial Training against l₁ Bounded Adversarial Attacks.

Yulun Jiang, Chen Liu, Zhichao Huang, Mathieu Salzmann, Sabine Süsstrunk

Proceedings of the International Conference on Machine Learning, 2023

2022

Robust Binary Models by Pruning Randomly-initialized Networks.

Chen Liu, Ziqi Zhao, Sabine Süsstrunk, Mathieu Salzmann

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2020

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them.

Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

On Certifying Robust Models by Polyhedral Envelope.

Chen Liu, Mathieu Salzmann, Sabine Süsstrunk

CoRR, 2019

On Certifying Non-uniform Bound against Adversarial Attacks.

Chen Liu, Ryota Tomioka, Volkan Cevher

CoRR, 2019

On Certifying Non-Uniform Bounds against Adversarial Attacks.

Chen Liu, Ryota Tomioka, Volkan Cevher

Proceedings of the 36th International Conference on Machine Learning, 2019

Finding Mixed Nash Equilibria of Generative Adversarial Networks.

Ya-Ping Hsieh, Chen Liu, Volkan Cevher

Proceedings of the 36th International Conference on Machine Learning, 2019