[2603.26569] Machine Unlearning under Retain-Forget Entanglement

Computer Science > Machine Learning arXiv:2603.26569 (cs) [Submitted on 27 Mar 2026] Title:Machine Unlearning under Retain-Forget Entanglement Authors:Jingpu Cheng, Ping Liu, Qianxiao Li, Chi Zhang View a PDF of the paper titled Machine Unlearning under Retain-Forget Entanglement, by Jingpu Cheng and 3 other authors View PDF HTML (experimental) Abstract:Forgetting a subset in machine unlearning is rarely an isolated task. Often, retained samples that are closely related to the forget set can be unintentionally affected, particularly when they share correlated features from pretraining or exhibit strong semantic similarities. To address this challenge, we propose a novel two-phase optimization framework specifically designed to handle such retai-forget entanglements. In the first phase, an augmented Lagrangian method increases the loss on the forget set while preserving accuracy on less-related retained samples. The second phase applies a gradient projection step, regularized by the Wasserstein-2 distance, to mitigate performance degradation on semantically related retained samples without compromising the unlearning objective. We validate our approach through comprehensive experiments on multiple unlearning tasks, standard benchmark datasets, and diverse neural architectures, demonstrating that it achieves effective and reliable unlearning while outperforming existing baselines in both accuracy retention and removal fidelity. Comments: Subjects: Machine Learning (cs.LG) Ci...