Asymmetric Langevin Unlearning uses public data to suppress unlearning noise costs by O(1/n_pub²), enabling practical mass unlearning with preserved utility under distribution mismatch.
month = oct, year =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
Asymmetric Langevin Unlearning uses public data to suppress unlearning noise costs by O(1/n_pub²), enabling practical mass unlearning with preserved utility under distribution mismatch.
-
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.