27 citations · 110 across the 24 of their papers we have counts for
11 papers · 1 filter
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…
Pre-trained Perceptual Features Improve Differentially Private Image Generation
Fredrik Harder, Milad Jalali Asadabadi, Danica J. Sutherland +1
Training even moderately-sized generative models with differentially-private stochastic gradient descent (DP-SGD) is difficult: the required level of noise for reasonable levels of…
Bayesian Importance of Features (BIF)
Kamil Adamczewski, Frederik Harder, Mijung Park
We introduce a simple and intuitive framework that provides quantitative explanations of statistical models through the probabilistic assessment of input feature importance. The co…
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…
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:…
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic
Complicated generative models often result in a situation where computing the likelihood of observed data is intractable, while simulating from the conditional density given a para…
Hierarchical models for neural population dynamics in the presence of non-stationarity
Mijung Park, Jakob H. Macke
Neural population activity often exhibits rich variability and temporal structure. This variability is thought to arise from single-neuron stochasticity, neural dynamics on short t…
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
Mijung Park, Wittawat Jitkrittum, Ahmad Qamar +3
We introduce the Locally Linear Latent Variable Model (LL-LVM), a probabilistic model for non-linear manifold discovery that describes a joint distribution over observations, their…