Matthew Regehr
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2026
Near-Optimal Pure Machine Unlearning for Smooth Strongly Convex Losses
The paper establishes tight upper and lower bounds on the statistical cost of approximate machine unlearning for smooth strongly convex losses, showing that the optimal unlearning rate depends critically on the relationship between the unlearning parameter $\varepsilon$ and the model dimension $d$.
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