4 citations · 6 across the 10 of their papers we have counts for
10 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…
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…
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…
Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement
Zhehao Huang, Xinwen Cheng, JingHao Zheng +4
Machine unlearning (MU) has emerged to enhance the privacy and trustworthiness of deep neural networks. Approximate MU is a practical method for large-scale models. Our investigati…
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection
Yingwen Wu, Ruiji Yu, Xinwen Cheng +2
In the open world, detecting out-of-distribution (OOD) data, whose labels are disjoint with those of in-distribution (ID) samples, is important for reliable deep neural networks (D…
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…
Friendly Sharpness-Aware Minimization
Tao Li, Pan Zhou, Zhengbao He +2
Sharpness-Aware Minimization (SAM) has been instrumental in improving deep neural network training by minimizing both training loss and loss sharpness. Despite the practical succes…
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…
Low-Dimensional Gradient Helps Out-of-Distribution Detection
Yingwen Wu, Tao Li, Xinwen Cheng +2
Detecting out-of-distribution (OOD) samples is essential for ensuring the reliability of deep neural networks (DNNs) in real-world scenarios. While previous research has predominan…