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

This post proposes an efficient unlearning framework that identifies subsets of training data with negligible impact on model outputs using influence functions. By reducing the size of datasets before unlearning, it achieves significant computational savings (up to approximately 50 percent) on real-world empirical examples.