1 citations · 1 across the 4 of their papers we have counts for
9 papers
SAGE: Retain-Aware Post-Hoc Sanitization of Final Unlearning Vector
Jingyuan Zhang, Yucheng Bai, Peixi Wen +6
Large Language Model (LLM) unlearning aims to remove undesirable knowledge or behaviors while preserving retained capabilities. Current unlearning methods all involve a trade-off b…
A Full-Pipeline Framework for Evaluating Membership Inference Attacks in Machine Learning
Ding Chen, Xinwen Cheng, Xuyang Zhong +3
While Membership Inference Attacks (MIAs) are the prevailing method for identifying training data, their application has expanded into privacy auditing and machine unlearning. Neve…
Remaining-data-free Machine Unlearning by Suppressing Sample Contribution
Xinwen Cheng, Zhehao Huang, Wenxin Zhou +4
Machine unlearning (MU) aims to remove the influence of specific training samples from a well-trained model, a task of growing importance due to the ``right to be forgotten.'' The…
Compensation-free Machine Unlearning in Text-to-Image Diffusion Models by Eliminating the Mutual Information
Xinwen Cheng, Jingyuan Zhang, Zhehao Huang +2
The powerful generative capabilities of diffusion models have raised growing privacy and safety concerns regarding generating sensitive or undesired content. In response, machine u…
Towards Natural Machine Unlearning
Zhengbao He, Tao Li, Xinwen Cheng +2
Machine unlearning (MU) aims to eliminate information that has been learned from specific training data, namely forgetting data, from a pre-trained model. Currently, the mainstream…
A Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning
Zhehao Huang, Xinwen Cheng, Jie Zhang +5
Recent advancements in deep models have highlighted the need for intelligent systems that combine continual learning (CL) for knowledge acquisition with machine unlearning (MU) for…