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Forget-only unlearning demands models memorize far more than standard training, challenging the premise of efficient deletion.
Private prediction can achieve logarithmic dependence on the time horizon, while private learning requires polynomial dependence, revealing a fundamental gap in their sample complexity for differentially private settings.
