1 citations · 1 across the 7 of their papers we have counts for
7 papers
Rendering Data Unlearnable by Exploiting LLM Alignment Mechanisms
Ruihan Zhang, Jun Sun
Large language models (LLMs) are increasingly trained on massive, heterogeneous text corpora, raising serious concerns about the unauthorised use of proprietary or personal data du…
Towards Provably Unlearnable Examples via Bayes Error Optimisation
Ruihan Zhang, Jun Sun, Ee-Peng Lim +1
The recent success of machine learning models, especially large-scale classifiers and language models, relies heavily on training with massive data. These data are often collected…
Correct-By-Construction: Certified Individual Fairness through Neural Network Training
Ruihan Zhang, Jun Sun
Fairness in machine learning is more important than ever as ethical concerns continue to grow. Individual fairness demands that individuals differing only in sensitive attributes r…
A Test Suite for Efficient Robustness Evaluation of Face Recognition Systems
Ruihan Zhang, Jun Sun
Face recognition is a widely used authentication technology in practice, where robustness is required. It is thus essential to have an efficient and easy-to-use method for evaluati…
Certified Robust Accuracy of Neural Networks Are Bounded due to Bayes Errors
Ruihan Zhang, Jun Sun
Adversarial examples pose a security threat to many critical systems built on neural networks. While certified training improves robustness, it also decreases accuracy noticeably.…
How Does Bayes Error Limit Probabilistic Robust Accuracy
Ruihan Zhang, Jun Sun
Adversarial examples pose a security threat to many critical systems built on neural networks. Given that deterministic robustness often comes with significantly reduced accuracy,…
Towards Certified Probabilistic Robustness with High Accuracy
Ruihan Zhang, Peixin Zhang, Jun Sun
Adversarial examples pose a security threat to many critical systems built on neural networks (such as face recognition systems, and self-driving cars). While many methods have bee…