9 citations · 15 across the 10 of their papers we have counts for
11 papers
Statistical Properties of Nonparametric MLE under Laplace Noise
Yifei Xiong, Nianqiao Phyllis Ju, Vinayak Rao
Local differential privacy (LDP) protects individuals in a dataset by perturbing each measurement before release. For real-valued data, a widely used mechanism is additive Laplace…
Redistricting from the Bottom Up: Sampling Communities of Interest with Differential Privacy
Atticus McWhorter, Caroline Hammond, Nianqiao Phyllis Ju +1
Independent Redistricting Commissions (IRCs) are a promising tool for bottom-up redistricting, but their public testimony processes are vulnerable to adversarial manipulation. We p…
SOMA: A Novel Sampler for Bayesian Inference from Privatized Data
Yifei Xiong, Nianqiao Phyllis Ju
Making valid statistical inferences from privatized data is a key challenge in modern analysis. In Bayesian settings, data augmentation MCMC (DAMCMC) methods impute unobserved conf…
dapper: Data Augmentation for Private Posterior Estimation in R
Kevin Eng, Jordan A. Awan, Nianqiao Phyllis Ju +2
This paper serves as a reference and introduction to using the R package dapper. dapper encodes a sampling framework which allows exact Markov chain Monte Carlo simulation of param…
Statistical Inference for Privatized Data with Unknown Sample Size
Jordan Awan, Andres Felipe Barrientos, Nianqiao Ju
We develop both theory and algorithms to analyze privatized data in unbounded differential privacy (DP), where even the sample size is considered a sensitive quantity that requires…
Spectral gap bounds for reversible hybrid Gibbs chains
Qian Qin, Nianqiao Ju, Guanyang Wang
Hybrid Gibbs samplers represent a prominent class of approximated Gibbs algorithms that utilize Markov chains to approximate conditional distributions, with the Metropolis-within-G…
Simulation-based Bayesian Inference from Privacy Protected Data
Yifei Xiong, Nianqiao Phyllis Ju, Sanguo Zhang
Many modern statistical analysis and machine learning applications require training models on sensitive user data. Under a formal definition of privacy protection, differentially p…
Data Augmentation MCMC for Bayesian Inference from Privatized Data
Nianqiao Ju, Jordan A. Awan, Ruobin Gong +1
Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform…
Sequential Monte Carlo algorithms for agent-based models of disease transmission
Nianqiao Ju, Jeremy Heng, Pierre E. Jacob
Agent-based models of disease transmission involve stochastic rules that specify how a number of individuals would infect one another, recover or be removed from the population. Co…
A simple Markov chain for independent Bernoulli variables conditioned on their sum
Jeremy Heng, Pierre E. Jacob, Nianqiao Ju
We consider a vector of independent binary variables, each with a different probability of success. The distribution of the vector conditional on its sum is known as the condit…
BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic
Qingyuan Zhao, Nianqiao Ju, Sergio Bacallado +1
The coronavirus disease 2019 (COVID-19) has quickly grown from a regional outbreak in Wuhan, China to a global pandemic. Early estimates of the epidemic growth and incubation perio…