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An Analysis of the Deployment of Models Trained on Private Tabular
  Synthetic Data: Unexpected Surprises

An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises

15 June 2021
Mayana Pereira
Meghana Kshirsagar
S. Mukherjee
Rahul Dodhia
J. L. Ferres
ArXivPDFHTML

Papers citing "An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises"

4 / 4 papers shown
Title
Synthetic Data -- what, why and how?
Synthetic Data -- what, why and how?
James Jordon
Lukasz Szpruch
F. Houssiau
M. Bottarelli
Giovanni Cherubin
Carsten Maple
Samuel N. Cohen
Adrian Weller
40
109
0
06 May 2022
Robin Hood and Matthew Effects: Differential Privacy Has Disparate
  Impact on Synthetic Data
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data
Georgi Ganev
Bristena Oprisanu
Emiliano De Cristofaro
37
57
0
23 Sep 2021
Reducing bias and increasing utility by federated generative modeling of
  medical images using a centralized adversary
Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary
Jean-Francois Rajotte
S. Mukherjee
Caleb Robinson
Anthony Ortiz
Christopher West
J. L. Ferres
R. Ng
FedML
MedIm
130
40
0
18 Jan 2021
Fairness in Machine Learning
Fairness in Machine Learning
L. Oneto
Silvia Chiappa
FaML
256
488
0
31 Dec 2020
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