27 citations · 110 across the 24 of their papers we have counts for
4 papers · 1 filter
Variational Bayes In Private Settings (VIPS)
Mijung Park, James Foulds, Kamalika Chaudhuri +1
Many applications of Bayesian data analysis involve sensitive information, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving fram…
Private Topic Modeling
Mijung Park, James Foulds, Kamalika Chaudhuri +1
We develop a privatised stochastic variational inference method for Latent Dirichlet Allocation (LDA). The iterative nature of stochastic variational inference presents challenges:…
A note on privacy preserving iteratively reweighted least squares
Mijung Park, Max Welling
Iteratively reweighted least squares (IRLS) is a widely-used method in machine learning to estimate the parameters in the generalised linear models. In particular, IRLS for L1 mini…
DP-EM: Differentially Private Expectation Maximization
Mijung Park, Jimmy Foulds, Kamalika Chaudhuri +1
The iterative nature of the expectation maximization (EM) algorithm presents a challenge for privacy-preserving estimation, as each iteration increases the amount of noise needed.…