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Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
Feb. 8, 2024, 5:10 a.m. | Yvonne Zhou Mingyu Liang Ivan Brugere Dana Dachman-Soled Danial Dervovic Antigoni Polychroniadou Min W
cs.CR updates on arXiv.org arxiv.org
contributed cs.cr cs.lg data dataset excess information linear machine machine learning may ml model private real reveal risk sensitive sensitive data synthetic synthetic data training training data
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