6 papers
On the Plasticity Collapse in Continual Machine Unlearning
Yingdan Shi, Xiang Xu, Kaize Ding +2
Machine unlearning enables deep neural networks to selectively remove the influence of specific data in response to privacy and regulatory requirements. While prior work largely st…
Beyond Sharp Minima: Robust LLM Unlearning via Feedback-Guided Multi-Point Optimization
Wenhan Wu, Zheyuan Liu, Chongyang Gao +2
Current LLM unlearning methods face a critical security vulnerability that undermines their fundamental purpose: while they appear to successfully remove sensitive or harmful knowl…
Latent Manifold Reconstruction and Representation with Topological and Geometrical Regularization
Ren Wang, Pengcheng Zhou
Manifold learning aims to discover and represent low-dimensional structures underlying high-dimensional data while preserving critical topological and geometric properties. Existin…
Exploring Nonlinear Pathway in Parameter Space for Machine Unlearning
Yingdan Shi, Ren Wang
Machine Unlearning (MU) aims to remove the information of specific training data from a trained model, ensuring compliance with privacy regulations and user requests. While one lin…
Tackling Fake Forgetting through Uncertainty Quantification
Yingdan Shi, Sijia Liu, Kaize Ding +1
Machine unlearning seeks to remove the influence of specified data from a trained model. While the unlearning accuracy provides a widely used metric for assessing unlearning perfor…
Deep Adversarial Defense Against Multilevel-Lp Attacks
Ren Wang, Yuxuan Li, Alfred Hero
Deep learning models have shown considerable vulnerability to adversarial attacks, particularly as attacker strategies become more sophisticated. While traditional adversarial trai…