8 citations · 10 across the 4 of their papers we have counts for
4 papers
Safeguarding Large Language Models: A Survey
Yi Dong, Ronghui Mu, Yanghao Zhang +9
In the burgeoning field of Large Language Models (LLMs), developing a robust safety mechanism, colloquially known as "safeguards" or "guardrails", has become imperative to ensure t…
Towards Fairness-Aware Adversarial Learning
Yanghao Zhang, Tianle Zhang, Ronghui Mu +2
Although adversarial training (AT) has proven effective in enhancing the model's robustness, the recently revealed issue of fairness in robustness has not been well addressed, i.e.…
Randomized Adversarial Training via Taylor Expansion
Gaojie Jin, Xinping Yi, Dengyu Wu +2
In recent years, there has been an explosion of research into developing more robust deep neural networks against adversarial examples. Adversarial training appears as one of the m…
3DVerifier: Efficient Robustness Verification for 3D Point Cloud Models
Ronghui Mu, Wenjie Ruan, Leandro S. Marcolino +1
3D point cloud models are widely applied in safety-critical scenes, which delivers an urgent need to obtain more solid proofs to verify the robustness of models. Existing verificat…