24 citations · 95 across the 7 of their papers we have counts for
7 papers
Differentially Private Bayesian Linear Regression
Garrett Bernstein, Daniel Sheldon
Linear regression is an important tool across many fields that work with sensitive human-sourced data. Significant prior work has focused on producing differentially private point…
Differentially Private Bayesian Inference for Exponential Families
Garrett Bernstein, Daniel Sheldon
The study of private inference has been sparked by growing concern regarding the analysis of data when it stems from sensitive sources. We present the first method for private Baye…
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models
Garrett Bernstein, Ryan McKenna, Tao Sun +3
We investigate the problem of learning discrete, undirected graphical models in a differentially private way. We show that the approach of releasing noisy sufficient statistics usi…
Consistently Estimating Markov Chains with Noisy Aggregate Data
Garrett Bernstein, Daniel Sheldon
We address the problem of estimating the parameters of a time-homogeneous Markov chain given only noisy, aggregate data. This arises when a population of individuals behave indepen…
Bayesian Discovery of Threat Networks
Steven T. Smith, Edward K. Kao, Kenneth D. Senne +2
A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat ne…
Stochastic Agent-Based Simulations of Social Networks
Garrett Bernstein, Kyle O'Brien
The rapidly growing field of network analytics requires data sets for use in evaluation. Real world data often lack truth and simulated data lack narrative fidelity or statistical…