papers
Publications (14)
cs.LG2016
Consistently Estimating Markov Chains with Noisy Aggregate Data
Garrett Bernstein, Daniel Sheldon
stat.ML2016
Bethe Projections for Non-Local Inference
Luke Vilnis, David Belanger, Daniel Sheldon +1
stat.ML2018
Learning in Integer Latent Variable Models with Nested Automatic Differentiation
Daniel Sheldon, Kevin Winner, Debora Sujono
cs.LG2019
Graphical-model based estimation and inference for differential privacy
Ryan McKenna, Daniel Sheldon, Gerome Miklau
cs.SI2013
Collective Diffusion Over Networks: Models and Inference
Akshat Kumar, Daniel Sheldon, Biplav Srivastava
cs.CC2013
Hamming Approximation of NP Witnesses
Daniel Sheldon, Neal E. Young
cs.LG2018
Differentially Private Bayesian Inference for Exponential Families
Garrett Bernstein, Daniel Sheldon
math.PR2011
First Passage Time of Skew Brownian Motion
Thilanka Appuhamillage, Daniel Sheldon
cs.LG2018
Importance Weighting and Variational Inference
Justin Domke, Daniel Sheldon
cs.LG2014
Gaussian Approximation of Collective Graphical Models
Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich
cs.SI2012
Maximizing the Spread of Cascades Using Network Design
Daniel Sheldon, Bistra Dilkina, Adam N. Elmachtoub +8
cs.LG2017
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models
Garrett Bernstein, Ryan McKenna, Tao Sun +3
cs.CV2019
A Bayesian Perspective on the Deep Image Prior
Zezhou Cheng, Matheus Gadelha, Subhransu Maji +1
cs.AI2016
Robust Optimization for Tree-Structured Stochastic Network Design
Xiaojian Wu, Akshat Kumar, Daniel Sheldon +1