On the importance of multiple training seeds for evaluating machine unlearning 事件

On the importance of multiple training seeds for evaluating machine unlearning arXiv:2510.26714v5 Announce Type: replace-cross Abstract: Machine unlearning aims to remove the influence of certain data points from a trained model without costly retraining. Most practical unlearning algorithms are only approximate and their performance can only be assessed empirically. Common practice is to run unlearning algorithms multiple times independently (i.e., using multiple unlearning seeds) starting fro