ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
(2025) On the neural feature ansatz for deep neural networks
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Tuning the depth-width ratio positions models in an efficient neural interaction interval that correlates with better generalization under fixed budgets and remains stable with scale.
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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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Law of Neural Interaction: Depth-Width Shape, Interaction Efficiency, and Generalization
Tuning the depth-width ratio positions models in an efficient neural interaction interval that correlates with better generalization under fixed budgets and remains stable with scale.