empirical-unlearning

Proposes a practical auditor that uses membership inference attacks to compute data-dependent lower bounds on the unlearning parameter, finding a sharp separation between certified algorithms (e.g., model clipping, rewind-to-delete) that achieve tight bounds and empirical methods (e.g., Hessian-based unlearning, gradient ascent) that exhibit large bounds, indicating poor unlearning.