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Generative models are used in a wide range of applications building on large amounts of contextually rich information.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Nonlinear component analysis as a kernel eigenvalue problem
B. Schölkopf, A. J. Smola, and K. Müller · 1998
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k k -anonymity: A model for protecting privacy
L. Sweeney · 2002
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Kernel k-means: spectral clustering and normalized cuts
I. S. Dhillon, Y. Guan, and B. Kulis · 2004
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On k-anonymity and the curse of dimensionality
C. C. Aggarwal · 2005
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Practical privacy: the SuLQ framework
A. Blum, C. Dwork, F. McSherry, and K. Nissim · 2005
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Random features for large-scale kernel machines
A. Rahimi and B. Recht · 2007
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How protective are synthetic data?
J. M. Abowd and L. Vilhuber · 2008
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The cost of privacy: destruction of data-mining utility in anonymized data publishing
J. Brickell and V. Shmatikov · 2008
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Privacy-preserving imputation of missing data
G. Jagannathan and R. N. Wright · 2008
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Privacy: Theory meets practice on the map
A. Machanavajjhala, D. Kifer, J. Abowd, J. Gehrke, and L. Vilhuber · 2008
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Training restricted boltzmann machines using approximations to the likelihood gradient
T. Tieleman · 2008
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Visualizing data using t-SNE
L. van der Maaten and G. Hinton · 2008
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Estimating risks of identification disclosure in partially synthetic data
J. P. Reiter and R. Mitra · 2009
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Boosting and differential privacy
C. Dwork, G. Rothblum, and S. Vadhan · 2010
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Generalized RBF feature maps for efficient detection
S. Vempati, A. Vedaldi, A. Zisserman, and C. V. Jawahar · 2010
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A statistical framework for differential privacy
L. Wasserman and S. Zhou · 2010
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How can we analyze differentially-private synthetic datasets?
A.-S. Charest · 2011
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Differentially private empirical risk minimization
K. Chaudhuri, C. Monteleoni, and A. D. Sarwate · 2011
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Publishing set-valued data via differential privacy
R. Chen, N. Mohammed, B. C. Fung, B. C. Desai, and L. Xiong · 2011
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ireduct: differential privacy with reduced relative errors
X. Xiao, G. Bender, M. Hay, and J. Gehrke · 2011
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Efficient kernel clustering using random fourier features
R. Chitta, R. Jin, and A. K. Jain · 2012
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A simple and practical algorithm for differentially private data release
M. Hardt, K. Ligett, and F. McSherry · 2012
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Differential privacy and statistical disclosure risk measures: An investigation with binary synthetic data
D. McClure and J. P. Reiter · 2012
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Generalized denoising auto-encoders as generative models
Y. Bengio, L. Yao, G. Alain, and P. Vincent · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
R. Bassily, A. Smith, and A. Thakurta · 2014
Differentially private data synthesis methods
C. M. Bowen and F. Liu · 2016
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Deep learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2016
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Model-based differential private data synthesis
F. Liu · 2016
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Improved techniques for training gans
T. Salimans, I. J. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Differentially private k-means clustering
D. Su, J. Cao, N. Li, E. Bertino, and H. Jin · 2016
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The Algorithmic Foundations of Differential Privacy
C. Dwork and A. Roth · 2014
Cited alongside, same era.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Deep embedding network for clustering
P. Huang, Y. Huang, W. Wang, and L. Wang · 2014
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2014
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Differentially private synthesization of multi-dimensional data using copula functions
H. Li, L. Xiong, and X. Jiang · 2014
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Differentially private stochastic gradient descent for in-RDBMS analytics
X. Wu, A. Kumar, K. Chaudhuri, S. Jha, and J. F. Naughton · 2016
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Unsupervised deep embedding for clustering analysis
J. Xie, R. Girshick, and A. Farhadi · 2016
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Unsupervised deep embedding for clustering analysis
J. Xie, R. B. Girshick, and A. Farhadi · 2016
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Towards k-means-friendly spaces: Simultaneous deep learning and clustering
B. Yang, X. Fu, N. D. Sidiropoulos, and M. Hong · 2016
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Joint unsupervised learning of deep representations and image clusters
J. Yang, D. Parikh, and D. Batra · 2016
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Semantic image inpainting with perceptual and contextual losses
R. Yeh, C. Chen, T. Lim, M. Hasegawa-Johnson, and M. N. Do · 2016
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Variational deep embedding: A generative approach to clustering
Y. Zheng, H. Tan, B. Tang, H. Zhou, et al · 2016
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Privacy-preserving generative deep neural networks support clinical data sharing
B. K. Beaulieu-Jones, Z. S. Wu, C. Williams, and C. S. Greene · 2017
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Plausible deniability for privacy-preserving data synthesis
V. Bindschaedler, R. Shokri, and C. A. Gunter · 2017
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Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization
K. G. Dizaji, A. Herandi, C. Deng, W. Cai, and H. Huang · 2017
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Cnn-based joint clustering and representation learning with feature drift compensation for large-scale image data
C.-C. Hsu and C.-W. Lin · 2017
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Discriminatively boosted image clustering with fully convolutional auto-encoders
F. Li, H. Qiao, B. Zhang, and X. Xi · 2017
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Semi-supervised knowledge transfer for deep learning from private training data
N. Papernot, M. Abadi, Ú. Erlingsson, I. J. Goodfellow, and K. Talwar · 2017
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Lossy image compression with compressive autoencoders
L. Theis, W. Shi, A. Cunningham, and F. Huszár · 2017
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