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Found 11 papers, 0 papers with code
Mixed Membership Stochastic Blockmodels
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.
A survey of statistical network models
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.
A Bayesian Approach to Graphical Record Linkage and De-duplication
We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files.
Methodology
SMERED: A Bayesian Approach to Graphical Record Linkage and De-duplication
We propose a novel unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files.
Computation Applications
A Comparison of Blocking Methods for Record Linkage
Record linkage seeks to merge databases and to remove duplicates when unique identifiers are not available.
Databases Applications
Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases
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.
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle
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.
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
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.
A Minimax Theory for Adaptive Data Analysis
In this paper, we propose a minimax framework for adaptive data analysis.
On-Average KL-Privacy and its equivalence to Generalization for Max-Entropy Mechanisms
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.
Dynamic Question Ordering in Online Surveys
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.
