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Modeling the probability distribution of rows in tabular data and generating realistic synthetic data is a non-trivial task.
Approximating discrete probability distributions with dependence trees
C Chow and Cong Liu · 1968
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Using cart to generate partially synthetic public use microdata
Jerome P Reiter · 2005
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Pattern recognition and machine learning
Christopher M Bishop · 2006
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MNIST handwritten digit database, 2010
Yann LeCun and Corinna Cortes · 2010
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Differentially private spatial decompositions
Graham Cormode, Cecilia Procopiuc, Divesh Srivastava, Entong Shen, and Ting Yu · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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The synthetic data vault
Neha Patki, Roy Wedge, and Kalyan Veeramachaneni · 2016
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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2016
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Privtree: A differentially private algorithm for hierarchical decompositions
Jun Zhang, Xiaokui Xiao, and Xing Xie · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Boosting deep learning risk prediction with generative adversarial networks for electronic health records
Zhengping Che, Yu Cheng, Shuangfei Zhai, Zhaonan Sun, and Yan Liu · 2017
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Generating multi-label discrete patient records using generative adversarial networks
Edward Choi, Siddharth Biswal, Bradley Malin, Jon Duke, Walter F. Stewart, and Jimeng Sun · 2017
Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
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Privbayes: Private data release via bayesian networks
Jun Zhang, Graham Cormode, Cecilia M Procopiuc, Divesh Srivastava, and Xiaokui Xiao · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Generating synthetic but plausible healthcare record datasets
Laura Aviñó, Matteo Ruffini, and Ricard Gavaldà · 2018
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Generating multi-categorical samples with generative adversarial networks
Ramiro Camino, Christian Hammerschmidt, and Radu State · 2018
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Pacgan: The power of two samples in generative adversarial networks
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Veegan: Reducing mode collapse in gans using implicit variational learning
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
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Generative adversarial networks for electronic health records: A framework for exploring and evaluating methods for predicting drug-induced laboratory test trajectories
Alexandre Yahi, Rami Vanguri, Noémie Elhadad, and Nicholas P Tatonetti · 2017
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Zinan Lin, Ashish Khetan, Giulia Fanti, and Sewoong Oh · 2018
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Data synthesis based on generative adversarial networks
Noseong Park, Mahmoud Mohammadi, Kshitij Gorde, Sushil Jajodia, Hongkyu Park, and Youngmin Kim · 2018
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Learning vine copula models for synthetic data generation
Yi Sun, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 2018
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Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela van der Schaar · 2019
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