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.
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Context-ordinal Nash equilibria are defined via social choice aggregation of ordinal preferences, shown to exist under mild conditions, with regularization, approximation, regret notions, complexity results, and learning rules developed.
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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.
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Nash without Numbers: A Social Choice Approach to Mixed Equilibria in Context-Ordinal Games
Context-ordinal Nash equilibria are defined via social choice aggregation of ordinal preferences, shown to exist under mild conditions, with regularization, approximation, regret notions, complexity results, and learning rules developed.