Ioannis Mitliagkas
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2026
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
The paper introduces Asymmetric Langevin Unlearning (ALU), a novel framework that uses public data to significantly reduce the utility loss typically associated with certified machine unlearning, enabling mass unlearning while preserving model performance.
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