ROSU derives a closed-form retain-neutral perturbation for min-max unlearning that bounds retain damage via curvature and improves performance when gradients are aligned.
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CoUn emulates retrained-model behavior on forget data by using contrastive learning on retain data to adjust semantic representations while preserving retain clusters via supervised learning, outperforming prior MU methods in experiments.
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Retain-Neutral Surrogates for Min-Max Unlearning
ROSU derives a closed-form retain-neutral perturbation for min-max unlearning that bounds retain damage via curvature and improves performance when gradients are aligned.
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CoUn: Empowering Machine Unlearning via Contrastive Learning
CoUn emulates retrained-model behavior on forget data by using contrastive learning on retain data to adjust semantic representations while preserving retain clusters via supervised learning, outperforming prior MU methods in experiments.