"adversarial robustness" Papers
68 papers found • Page 2 of 2
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off
Yuansan Liu, Ruqing Zhang, Mingkun Zhang et al.
Precise Accuracy / Robustness Tradeoffs in Regression: Case of General Norms
Elvis Dohmatob, Meyer Scetbon
Rethinking Adversarial Robustness in the Context of the Right to be Forgotten
Chenxu Zhao, Wei Qian, Yangyi Li et al.
Rethinking Fast Adversarial Training: A Splitting Technique To Overcome Catastrophic Overfitting
Masoumeh Zareapoor, Pourya Shamsolmoali
Robust Classification via a Single Diffusion Model
Huanran Chen, Yinpeng Dong, Zhengyi Wang et al.
Robustness Tokens: Towards Adversarial Robustness of Transformers
Brian Pulfer, Yury Belousov, Slava Voloshynovskiy
Robust Stable Spiking Neural Networks
Ding Jianhao, Zhiyu Pan, Yujia Liu et al.
Robust Universal Adversarial Perturbations
Changming Xu, Gagandeep Singh
Robust Yet Efficient Conformal Prediction Sets
Soroush H. Zargarbashi, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski
SHINE: Shielding Backdoors in Deep Reinforcement Learning
Zhuowen Yuan, Wenbo Guo, Jinyuan Jia et al.
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization
Xixu Hu, Runkai Zheng, Jindong Wang et al.
The Perception-Robustness Tradeoff in Deterministic Image Restoration
Guy Ohayon, Tomer Michaeli, Michael Elad
The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks
Ziquan Liu, Yufei Cui, Yan Yan et al.
Towards Optimal Adversarial Robust Q-learning with Bellman Infinity-error
Haoran Li, Zicheng Zhang, Wang Luo et al.
Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models
Francesco Croce, Naman D. Singh, Matthias Hein
Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection
Nils Palumbo, Yang Guo, Xi Wu et al.
Two Tales of Single-Phase Contrastive Hebbian Learning
Rasmus Kjær Høier, Christopher Zach
VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees
Anahita Baninajjar, Ahmed Rezine, Amir Aminifar