The first integrated taxonomy, empirical study of interplay and shallow dememorization, plus a theoretical guarantee on dememorization depth for certified unlearning.
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ManiF-SMC uses manifold representation forgetting with self-mode connectivity to achieve approximate unlearning comparable to SOTA methods on four datasets while staying in representation space.
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SoK: Unlearnability and Unlearning for Model Dememorization
The first integrated taxonomy, empirical study of interplay and shallow dememorization, plus a theoretical guarantee on dememorization depth for certified unlearning.
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Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity
ManiF-SMC uses manifold representation forgetting with self-mode connectivity to achieve approximate unlearning comparable to SOTA methods on four datasets while staying in representation space.