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.
2.(Isotropic Gaussian conditionals)conditional on classi, θ(x)|(y=i)∼ N(µ i, σ2Id), with ∥µi∥= 1 and {µi}C i=1 coinciding with the ETF directions{v i}from NC (i.e.µ i =v i)
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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.