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Differentially Private Learning of Undirected Graphical Models using
  Collective Graphical Models

Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models

14 June 2017
G. Bernstein
Ryan McKenna
Tao Sun
Daniel Sheldon
Michael Hay
G. Miklau
    FedML
ArXivPDFHTML

Papers citing "Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models"

14 / 14 papers shown
Title
Deep Learning with Differential Privacy
Deep Learning with Differential Privacy
Martín Abadi
Andy Chu
Ian Goodfellow
H. B. McMahan
Ilya Mironov
Kunal Talwar
Li Zhang
FedML
SyDa
175
6,069
0
01 Jul 2016
Bolt-on Differential Privacy for Scalable Stochastic Gradient
  Descent-based Analytics
Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics
Xi Wu
Fengan Li
Arun Kumar
Kamalika Chaudhuri
S. Jha
Jeffrey F. Naughton
22
20
0
15 Jun 2016
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis
James R. Foulds
J. Geumlek
Max Welling
Kamalika Chaudhuri
47
102
0
23 Mar 2016
Bethe Projections for Non-Local Inference
Bethe Projections for Non-Local Inference
Luke Vilnis
David Belanger
Daniel Sheldon
Andrew McCallum
51
24
0
04 Mar 2015
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo
Yu Wang
S. Fienberg
Alex Smola
51
248
0
26 Feb 2015
Differentially Private Exponential Random Graphs
Differentially Private Exponential Random Graphs
Vishesh Karwa
Aleksandra B. Slavkovic
P. Krivitsky
66
42
0
16 Sep 2014
Differentially Private Empirical Risk Minimization: Efficient Algorithms
  and Tight Error Bounds
Differentially Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds
Raef Bassily
Adam D. Smith
Abhradeep Thakurta
FedML
111
371
0
27 May 2014
Gaussian Approximation of Collective Graphical Models
Gaussian Approximation of Collective Graphical Models
Li-Ping Liu
Daniel Sheldon
Thomas G. Dietterich
37
9
0
20 May 2014
Bayesian Differential Privacy through Posterior Sampling
Bayesian Differential Privacy through Posterior Sampling
Christos Dimitrakakis
B. Nelson
and Zuhe Zhang
Aikaterini Mitrokotsa
Benjamin I. P. Rubinstein
58
118
0
05 Jun 2013
Maximum likelihood estimation in log-linear models
Maximum likelihood estimation in log-linear models
S. Fienberg
Alessandro Rinaldo
50
80
0
19 Apr 2011
Differentially Private Empirical Risk Minimization
Differentially Private Empirical Risk Minimization
Kamalika Chaudhuri
C. Monteleoni
Anand D. Sarwate
95
1,482
0
01 Dec 2009
Learning in a Large Function Space: Privacy-Preserving Mechanisms for
  SVM Learning
Learning in a Large Function Space: Privacy-Preserving Mechanisms for SVM Learning
Benjamin I. P. Rubinstein
Peter L. Bartlett
Ling Huang
N. Taft
88
293
0
30 Nov 2009
Efficient, Differentially Private Point Estimators
Efficient, Differentially Private Point Estimators
Adam D. Smith
FedML
68
78
0
27 Sep 2008
What Can We Learn Privately?
What Can We Learn Privately?
S. Kasiviswanathan
Homin K. Lee
Kobbi Nissim
Sofya Raskhodnikova
Adam D. Smith
101
1,459
0
06 Mar 2008
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