ALU uses public data to suppress unlearning cost quadratically while characterizing distribution mismatch effects, enabling mass unlearning with maintained utility.
arXiv preprint arXiv:2303.01861 , year=
2 Pith papers cite this work, alongside 8 external citations. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.
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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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Statistical Properties of Training & Generalization
Review of neural scaling laws and their relation to constraints and inductive biases when applying machine learning to physics problems.