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120 !
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Mon, 8/9/2021,
1:30 PM -
3:20 PM
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Virtual
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Challenges and Recent Advances in Private Data Analysis — Topic-Contributed Papers
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Section on Nonparametric Statistics, IMS, CHANCE, Section on Statistical Learning and Data Science
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Organizer(s): Linjun Zhang, Rutgers University
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Chair(s): Linjun Zhang, Rutgers University
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1:35 PM
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The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds
Yichen Wang, University of Pennsylvania; Tony Cai, University of Pennsylvania; Linjun Zhang, Rutgers University
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1:55 PM
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Differentially Private Statistics for Collaborative Neuroinformatics
Anand Sarwate, Rutgers University
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2:15 PM
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Interactive Versus Non-Interactive Locally Differentially Private Estimation: Two Elbows for the Quadratic Functional
Lukas Steinberger, University of Vienna
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2:35 PM
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A Central Limit Theorem and Uncertainty Principle for Differentially Private Query Answering
Jinshuo Dong, Northwestern University
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2:55 PM
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High-Dimensional, Differentially-Private EM Algorithm: Methods and Near Optimal Statistical Guarantees
Zhe Zhang, Rutgers University, New Brunswick; Linjun Zhang, Rutgers University
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3:15 PM
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Floor Discussion
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