ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
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.
-
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.