The paper establishes that the optimal excess risk for ε-unlearning is the usual statistical error plus an unlearning penalty that interpolates between retraining-from-scratch and an exponentially smaller term as ε/d grows, with matching bounds for mean estimation.
arXiv preprint arXiv:2301.00457 , year=
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Near-Optimal Pure Machine Unlearning for Smooth Strongly Convex Losses
The paper establishes that the optimal excess risk for ε-unlearning is the usual statistical error plus an unlearning penalty that interpolates between retraining-from-scratch and an exponentially smaller term as ε/d grows, with matching bounds for mean estimation.