Apple · Apple Machine Learning
As concerns around data privacy in machine learning grow, the ability to unlearn—or remove—specific data points from trained
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While state-of-the-art unlearning methods have emerged in response, they typically treat all points in the forget set equally.
Key facts
- When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs
- Authors Anat Kleiman†**, Robert Fisher, Ben Deaner‡, Udi Wieder, Vitaly Feldman
- Leveraging this insight, they propose an efficient unlearning framework that reduces the size of datasets before unlearning—leading to significant computational savings (up to ~50%) on real-world
- As concerns around data privacy in machine learning grow, the ability to unlearn—or remove—specific data points from trained models becomes increasingly important
Summary
When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs. Authors Anat Kleiman†**, Robert Fisher, Ben Deaner‡, Udi Wieder, Vitaly Feldman. As concerns around data privacy in machine learning grow, the ability to unlearn—or remove—specific data points from trained models becomes increasingly important. Leveraging this insight, they propose an efficient unlearning framework that reduces the size of datasets before unlearning—leading to significant computational savings (up to ~50%) on real-world empirical examples.