ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
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LAMP reduces optimal transport storage to linear space O(n+m) while preserving last-iterate convergence rates of primal-dual mirror prox and scaling to n=m=2^18.
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Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
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Log-Averaged Mirror Prox for Fast, Large-Scale Optimal Transport in Linear Space
LAMP reduces optimal transport storage to linear space O(n+m) while preserving last-iterate convergence rates of primal-dual mirror prox and scaling to n=m=2^18.