Influence-derived data perturbations are evaluated for three roles in machine unlearning, including as a direct deletion signal, a utility-preserving regularizer, and a warm start for adversarial unlearning.
Read the original at arxiv.org→arXiv:2609.12313v1 Announce Type: new Abstract: We evaluate Deep Perturbation Learning (DPL), which perturbs training images and labels along influence-derived directions, in three roles in which prior work has...
Original headline: "Do Influence-Derived Data Perturbations Enable Machine Unlearning? A Controlled Study of Three Plausible Roles"
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- Sep 14, 04:00 UTC arXiv cs.AI lead source Do Influence-Derived Data Perturbations Enable Machine Unlearning? A Controlled Study of Three Plausible Roles