Investigating Statistical Privacy Frameworks from the Perspective of Hypothesis Testing von Liu, Changchang, He, Xi, Chanyaswad, Thee, Wang, Shiqiang, Mittal, Prateek

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RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release von Chanyaswad, Thee, Liu, Changchang, Mittal, Prateek

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Collaborative PCA/DCA Learning Methods for Compressive Privacy von Kung, Sun-Yuan, Chanyaswad, Thee, Chang, J. Morris, Wu, Peiyuan

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A compressive multi-kernel method for privacy-preserving machine learning von Chanyaswad, Thee, Chang, J. Morris, Kung, S. Y

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Supervising Nystr\"om Methods via Negative Margin Support Vector Selection von Al, Mert, Chanyaswad, Thee, Kung, Sun-Yuan

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RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release von Chanyaswad, Thee, Liu, Changchang, Mittal, Prateek

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Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning von Filipowicz, Artur, Chanyaswad, Thee, Kung, S. Y

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A Differential Privacy Mechanism Design Under Matrix-Valued Query von Chanyaswad, Thee, Dytso, Alex, Poor, H. Vincent, Mittal, Prateek

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MVG Mechanism: Differential Privacy under Matrix-Valued Query von Chanyaswad, Thee, Dytso, Alex, Poor, H. Vincent, Mittal, Prateek

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A compressive multi-kernel method for privacy-preserving machine learning von Chanyaswad, Thee, J Morris Chang, Kung, S Y

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Supervising Nyström Methods via Negative Margin Support Vector Selection von Mert Al, Chanyaswad, Thee, Sun-Yuan, Kung

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Coupling Random Orthonormal Projection with Gaussian Generative Model for Non-Interactive Private Data Release von Chanyaswad, Thee, Liu, Changchang, Mittal, Prateek

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Desensitized RDCA Subspaces for Compressive Privacy in Machine Learning von Filipowicz, Artur, Chanyaswad, Thee, Kung, S Y

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MVG Mechanism: Differential Privacy under Matrix-Valued Query von Chanyaswad, Thee, Dytso, Alex, Poor, H Vincent, Mittal, Prateek

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A Differential Privacy Mechanism Design Under Matrix-Valued Query von Chanyaswad, Thee, Dytso, Alex, Poor, H Vincent, Mittal, Prateek

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