Search Results for author: Stephen E. Fienberg

Found 11 papers, 0 papers with code

Dynamic Question Ordering in Online Surveys

no code implementations14 Jul 2016 Kirstin Early, Jennifer Mankoff, Stephen E. Fienberg

In the general survey-taking setting, we want to maximize survey completion, and so we focus on ordering questions to engage the respondent and collect hopefully all information, or at least the information that most characterizes the respondent, for accurate imputations.

Imputation

On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms

no code implementations8 May 2016 Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg

We define On-Average KL-Privacy and present its properties and connections to differential privacy, generalization and information-theoretic quantities including max-information and mutual information.

A Minimax Theory for Adaptive Data Analysis

no code implementations13 Feb 2016 Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg

In this paper, we propose a minimax framework for adaptive data analysis.

Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo

no code implementations26 Feb 2015 Yu-Xiang Wang, Stephen E. Fienberg, Alex Smola

We consider the problem of Bayesian learning on sensitive datasets and present two simple but somewhat surprising results that connect Bayesian learning to "differential privacy:, a cryptographic approach to protect individual-level privacy while permiting database-level utility.

Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle

no code implementations23 Feb 2015 Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg

Lastly, we extend some of the results to the more practical $(\epsilon,\delta)$-differential privacy and establish the existence of a phase-transition on the class of problems that are approximately privately learnable with respect to how small $\delta$ needs to be.

Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases

no code implementations30 Jul 2014 Fei Yu, Michal Rybar, Caroline Uhler, Stephen E. Fienberg

Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for releasing GWAS data in a privacy-preserving way.

A Comparison of Blocking Methods for Record Linkage

no code implementations11 Jul 2014 Rebecca C. Steorts, Samuel L. Ventura, Mauricio Sadinle, Stephen E. Fienberg

Record linkage seeks to merge databases and to remove duplicates when unique identifiers are not available.

Databases Applications

SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication

no code implementations2 Mar 2014 Rebecca C. Steorts, Rob Hall, Stephen E. Fienberg

We propose a novel unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files.

Computation Applications

A Bayesian Approach to Graphical Record Linkage and De-duplication

no code implementations17 Dec 2013 Rebecca C. Steorts, Rob Hall, Stephen E. Fienberg

We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files.

Methodology

A survey of statistical network models

no code implementations29 Dec 2009 Anna Goldenberg, Alice X. Zheng, Stephen E. Fienberg, Edoardo M. Airoldi

Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation.

Mixed Membership Stochastic Blockmodels

no code implementations NeurIPS 2008 Edo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing

Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks.

Variational Inference

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