A penalty-based bi-level optimization framework for machine unlearning that decorrelates forget and retention gradients via inner maximization and restores utility via outer minimization, with convergence guarantees and improved trade-offs on vision and language benchmarks.
Snap: Unlearning selective knowledge in large language models with negative instructions.arXiv preprint arXiv:2406.12329,
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OFMU: Optimization-Driven Framework for Machine Unlearning
A penalty-based bi-level optimization framework for machine unlearning that decorrelates forget and retention gradients via inner maximization and restores utility via outer minimization, with convergence guarantees and improved trade-offs on vision and language benchmarks.
- Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data