no code implementations • 25 May 2023 • Saiyue Lyu, Michael F. Liu, Margarita Vinaroz, Mijung Park
In this paper, we further improve the current state of DMs with DP by adopting the Latent Diffusion Models (LDMs).
2 code implementations • 31 Jan 2023 • 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.
no code implementations • 25 Nov 2021 • Margarita Vinaroz, Mijung Park
We provide a theoretical analysis of the privacy-accuracy trade-off in the posterior estimates under our method, called differentially private stochastic expectation propagation (DP-SEP).
1 code implementation • 9 Jun 2021 • Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, Mijung Park
Hence, a relatively low order of Hermite polynomial features can more accurately approximate the mean embedding of the data distribution compared to a significantly higher number of random features.
no code implementations • 11 Oct 2019 • Mijung Park, Margarita Vinaroz, Wittawat Jitkrittum
SVT incurs the privacy cost only when a condition (whether a quantity of interest is above/below a threshold) is met.