Apple Research: Low-Influence Data Points Can Be Unlearned for Free
Apple researchers propose that unlearning can be made cheaper by identifying training points with negligible influence on model outputs. Comparing influence functions across language and vision tasks, they show that such low-impact points can be removed without significant performance loss, reducing computational costs of machine unlearning.
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