4 citations · 8 across the 4 of their papers we have counts for
4 papers
DP-LDMs: Differentially Private Latent Diffusion Models
Michael F. Liu, Saiyue Lyu, Margarita Vinaroz +1
Diffusion models (DMs) are one of the most widely used generative models for producing high quality images. However, a flurry of recent papers points out that DMs are least private…
Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation
Margarita Vinaroz, Mi Jung Park
Data distillation aims to generate a small data set that closely mimics the performance of a given learning algorithm on the original data set. The distilled dataset is hence usefu…
Differentially private stochastic expectation propagation (DP-SEP)
Margarita Vinaroz, Mijung Park
We are interested in privatizing an approximate posterior inference algorithm called Expectation Propagation (EP). EP approximates the posterior by iteratively refining approximati…
Hermite Polynomial Features for Private Data Generation
Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder +2
Kernel mean embedding is a useful tool to represent and compare probability measures. Despite its usefulness, kernel mean embedding considers infinite-dimensional features, which a…