On the Plasticity Collapse in Continual Machine Unlearning
It is shown that continual unlearning operations accumulate geometric constraints in parameter space, leading to saturated subspaces that restrict future updates, a critical barrier to the long-term reliability of machine unlearning systems and motivate the development of plasticity-preserving unlearning algorithms.
Ying-Dan Shi, Xiang-Dong Xu, Kaize Ding et al.
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