POUR derives a provably optimal forgetting operator by showing that orthogonal projections of simplex equiangular tight frames remain ETFs in lower dimensions, enabling representation-level unlearning with closed-form and distillation variants.
Are we truly forgetting? a critical re-examination of machine unlearn- ing evaluation protocols.arXiv preprint arXiv:2503.06991
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POUR: A Provably Optimal Method for Unlearning Representations via Neural Collapse
POUR derives a provably optimal forgetting operator by showing that orthogonal projections of simplex equiangular tight frames remain ETFs in lower dimensions, enabling representation-level unlearning with closed-form and distillation variants.