  We address the challenge of efficiently and reliably deleting data from
machine learning models trained using Empirical Risk Minimization (ERM), a
process known as machine unlearning. To avoid retraining models from scratch,
we propose a novel algorithm leveraging Natural Gradient Descent (NGD). Our
theoretical framework ensures strong privacy guarantees for convex models,
while a practical Min/Max optimization algorithm is developed for non-convex
models. Comprehensive evaluations show significant improvements in privacy,
computational efficiency, and generalization compared to state-of-the-art
methods, advancing both the theoretical and practical aspects of machine
unlearning.
