3 papers
cs.LG2026
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…
cs.LG2025
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…
cs.LG2025
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…