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Activity Number: 268 - A Unifying Theme for Interpretable Information Extraction from Data: The Stability Principle
Type: Invited
Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #322076 View Presentation
Title: Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing
Author(s): Ryan Rogers and Aaron Roth* and Adam Smith and Om Thakkar
Companies: University of Pennsylvania and University of Pennsylvania and Pennsylvania State University and Penn State
Keywords: Selective Inference ; Post Selection Inference ; Hypothesis Testing ; Differential Privacy ; Adaptive Data Analysis
Abstract:

We study how the generalization properties of differential privacy can be used to perform adaptive hypothesis testing, while giving statistically valid p-value corrections. We do this by observing that the guarantees of algorithms with bounded "approximate max-information" are sufficient to correct the p-values of hypotheses which have been chosen with arbitrary degrees of adaptivity, and then by proving that algorithms that satisfy (epsilon,delta)-differential privacy have bounded approximate max information. The talk will be aimed at a general statistical audience who does not have background in differential privacy.


Authors who are presenting talks have a * after their name.

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