2 citations · 2 across the 3 of their papers we have counts for
4 papers
Evaluating LLM Personalization via Semantic Constraint Verification
Xuran Li, Guanqin Zhang, Imran Razzak +4
Current evaluation paradigms for Large Language Model (LLM) personalization rely heavily on brittle surface-matching metrics or computationally expensive LLM-as-a-judge protocols,…
Perturbation Effects on Robustness and Individual Fairness
Xuran Li, Hao Xue, Peng Wu +4
Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings.…
RobustFair: Adversarial Evaluation through Fairness Confusion Directed Gradient Search
Xuran Li, Peng Wu, Kaixiang Dong +2
Deep neural networks (DNNs) often face challenges due to their vulnerability to various adversarial perturbations, including false perturbations that undermine prediction accuracy…
Accurate Fairness: Improving Individual Fairness without Trading Accuracy
Xuran Li, Peng Wu, Jing Su
Accuracy and individual fairness are both crucial for trustworthy machine learning, but these two aspects are often incompatible with each other so that enhancing one aspect may sa…