Rongzhe Wei

SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation

2021 · arXiv (Cornell University)

Anomaly detection is a fundamental yet challenging problem in machine learning due to the lack of label information. In this work, we propose a novel and powerful framework, dubbed as SLA$^2$P, for unsupervised anomaly detection. …

Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning

2024 · arXiv (Cornell University)

Large Language Models (LLMs) embed sensitive, human-generated data, prompting the need for unlearning methods. Although certified unlearning offers strong privacy guarantees, its restrictive assumptions make it unsuitable for LLMs, giving rise to various heuristic approaches …