Class unlearning methods leak membership through neighbor-class output probabilities, and a tilted reweighting objective that mimics retrained models reduces this leakage.
Towards unbounded machine unlearning.Advances in neural information processing systems, 36:1957–1987,
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On the Necessity of Output Distribution Reweighting for Effective Class Unlearning
Class unlearning methods leak membership through neighbor-class output probabilities, and a tilted reweighting objective that mimics retrained models reduces this leakage.