When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs

Apple researchers have introduced a method to reduce the computational resources required to remove specific data from trained models by identifying and targeting only low-influence points. This approach improves efficiency by avoiding the standard practice of treating all data in a removal set with equal importance.