Near-Optimal Machine Unlearning Utility for Smooth Strongly Convex Losses
Machine unlearning is motivated by legal and user-facing requirements to remove the influence of individuals' data from trained models, such as the right to be forgotten. Prior work has developed algorithms and error bounds for unlearning in smooth strongly convex stochastic optimization but the fundamental statistical cost of unlearning has remained unclear. We nearly resolve this problem by proving upper and lower bounds on the excess population risk of approximate $(\varepsilon, δ)$-unlearning; our bounds are tight up to a condition-number factor. For mean estimation over the unit ball, our upper and lower bounds match. In fact, our algorithm achieves $\varepsilon$-unlearning, which im...
| Authors | Matthew Regehr, Gautam Kamath, Andrew Lowy |
|---|---|
| Primary category | cs.LG |
| Published | 1 June 2026 |
| Last updated at source | 17 September 2026 |
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