Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning

arXiv:2403.07362v1 Announce Type: new
Abstract: The trustworthy machine learning (ML) community is increasingly recognizing the crucial need for models capable of selectively 'unlearning' data points after training. This leads to the problem of machine unlearning (MU), aiming to eliminate the influence of chosen data points on model performance, while still maintaining the model's utility post-unlearning. Despite various MU methods for data influence erasure, evaluations have largely focused on random data forgetting, ignoring the vital inquiry into which subset should be …

arxiv case cs.ai cs.cv cs.lg machine type unlearning