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Unlearning can be exponentially more efficient than retraining from scratch when the unlearning requirement is sufficiently large, fundamentally changing the landscape of data removal in machine learning.
Pure differential privacy no longer lags behind approximate DP in heavy-tailed stochastic convex optimization: this work establishes matching minimax optimal rates.
