3 papers
cs.LG2026
The Unseen Threat: Residual Knowledge in Machine Unlearning under Perturbed Samples
Hsiang Hsu, Pradeep Niroula, Zichang He +3
Machine unlearning offers a practical alternative to avoid full model re-training by approximately removing the influence of specific user data. While existing methods certify unle…
cs.LG2025
PASS: Private Attributes Protection with Stochastic Data Substitution
Yizhuo Chen, Chun-Fu, Chen +3
The growing Machine Learning (ML) services require extensive collections of user data, which may inadvertently include people's private information irrelevant to the services. Vari…
cs.AI2024
Probing LLM Hallucination from Within: Perturbation-Driven Approach via Internal Knowledge
Seongmin Lee, Hsiang Hsu, Chun-Fu Chen +1
LLM hallucination, where unfaithful text is generated, presents a critical challenge for LLMs' practical applications. Current detection methods often resort to external knowledge,…