35 citations · 79 across the 8 of their papers we have counts for
4 papers · 1 filter
Differentially Private Bayesian Linear Regression
Garrett Bernstein, Daniel Sheldon
Linear regression is an important tool across many fields that work with sensitive human-sourced data. Significant prior work has focused on producing differentially private point…
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation
Justin Domke, Daniel Sheldon
Recent work in variational inference (VI) uses ideas from Monte Carlo estimation to tighten the lower bounds on the log-likelihood that are used as objectives. However, there is no…
A Bayesian Perspective on the Deep Image Prior
Zezhou Cheng, Matheus Gadelha, Subhransu Maji +1
The deep image prior was recently introduced as a prior for natural images. It represents images as the output of a convolutional network with random inputs. For "inference", gradi…
Graphical-model based estimation and inference for differential privacy
Ryan McKenna, Daniel Sheldon, Gerome Miklau
Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements. It is common to use this information to estimate the answers to new quer…