A new paper shows low-influence data points can be skipped during machine unlearning, cutting compute costs. The authors compare influence functions across language and vision tasks, identifying forget-set subsets with negligible impact on model outputs. This challenges the assumption that every point must be removed, offering a cheaper path to privacy compliance.
Opening Kapyn…