Theoretical characterization of the inlier-memorization effect in simple autoencoders, deriving its emergence, strength, and persistence from data distribution and initialization, plus guidelines achieving SOTA on ADBench.
2412.08501 , archivePrefix=
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INO-SGD down-weights data in each batch to improve model performance on strongly private data while satisfying individualized differential privacy constraints.
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What Drives the Inlier-Memorization Effect? A Theory of Outlier Detection via Early Training Dynamics
Theoretical characterization of the inlier-memorization effect in simple autoencoders, deriving its emergence, strength, and persistence from data distribution and initialization, plus guidelines achieving SOTA on ADBench.
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INO-SGD: Addressing Utility Imbalance under Individualized Differential Privacy
INO-SGD down-weights data in each batch to improve model performance on strongly private data while satisfying individualized differential privacy constraints.