Model Interpretability

Leveraging Per-Instance Privacy for Machine Unlearning

This research reveals a theoretical and empirical framework for understanding and quantifying the difficulty of machine unlearning for individual data points, showing that unlearning steps scale logarithmically with per-instance privacy loss.