Noisy fine-tuning with gradient or model clipping on retained data provably removes the influence of forget data, with guarantees that need no smoothness or convexity assumptions.
Privacy amplification by mixing and diffusion mechanisms
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Certified Unlearning for Neural Networks
Noisy fine-tuning with gradient or model clipping on retained data provably removes the influence of forget data, with guarantees that need no smoothness or convexity assumptions.