8 citations · 11 across the 5 of their papers we have counts for
3 papers · 1 filter
ABCDP: Approximate Bayesian Computation with Differential Privacy
Mijung Park, Margarita Vinaroz, Wittawat Jitkrittum
We develop a novel approximate Bayesian computation (ABC) framework, ABCDP, that produces differentially private (DP) and approximate posterior samples. Our framework takes advanta…
Radial and Directional Posteriors for Bayesian Neural Networks
Changyong Oh, Kamil Adamczewski, Mijung Park
We propose a new variational family for Bayesian neural networks. We decompose the variational posterior into two components, where the radial component captures the strength of ea…
A Differentially Private Kernel Two-Sample Test
Anant Raj, Ho Chung Leon Law, Dino Sejdinovic +1
Kernel two-sample testing is a useful statistical tool in determining whether data samples arise from different distributions without imposing any parametric assumptions on those d…