HAMU is a constrained-optimization unlearning method that uses forget-retain data similarity as a hardness measure to guarantee specified forget-quality gains while minimizing retain degradation.
Towards understanding the feasibility of machine unlearning
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How Hard Can It Be? Hardness-Aware Multi-Objective Unlearning
HAMU is a constrained-optimization unlearning method that uses forget-retain data similarity as a hardness measure to guarantee specified forget-quality gains while minimizing retain degradation.