Andrew Lowy
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The paper characterizes the minimax optimal excess-risk rate for pure $\varepsilon$-DP stochastic convex optimization with heavy-tailed gradients, providing an algorithm that achieves this rate.
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 $d$.
Papers
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 critica…