Faster Machine Unlearning via Natural Gradient Descent
<|reference_start|>Faster Machine Unlearning via Natural Gradient Descent: 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.<|reference_end|>
@article{lev2024faster,
    title={Faster Machine Unlearning via Natural Gradient Descent},
    author={Omri Lev and Ashia Wilson},
    journal={arXiv preprint arXiv:2407.08169},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08169},
    primaryClass={cs.LG cs.AI}
}