Fetching the paper…
Reading the bibliography…
The availability of rich and vast data sources has greatly advanced machine learning applications in various domains.
The information bottleneck method
Naftali Tishby, Fernando C. Pereira, and William Bialek · 1999
Earlier work this paper cites.
Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
Earlier work this paper cites.
Differential privacy: A survey of results
Cynthia Dwork · 2008
Earlier work this paper cites.
On the complexity of differentially private data release: efficient algorithms and hardness results
Cynthia Dwork, Moni Naor, Omer Reingold, Guy N Rothblum, and Salil Vadhan · 2009
Earlier work this paper cites.
Privacy-preserving data publishing: A survey of recent developments
Benjamin CM Fung, Ke Wang, Rui Chen, and Philip S Yu · 2010
Earlier work this paper cites.
A multiplicative weights mechanism for privacy-preserving data analysis
Moritz Hardt and Guy N Rothblum · 2010
Earlier work this paper cites.
Interactive privacy via the median mechanism
Aaron Roth and Tim Roughgarden · 2010
Earlier work this paper cites.
The neural autoregressive distribution estimator
Hugo Larochelle and Iain Murray · 2011
Earlier work this paper cites.
A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank McSherry · 2012
Earlier work this paper cites.
Wasserstein barycenter and its application to texture mixing
Julien Rabin, Gabriel Peyré, Julie Delon, and Marc Bernot · 2012
Earlier work this paper cites.
A learning theory approach to noninteractive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
Earlier work this paper cites.
Sliced and radon wasserstein barycenters of measures
Nicolas Bonneel, Julien Rabin, Gabriel Peyré, and Hanspeter Pfister · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
Earlier work this paper cites.
Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
Earlier work this paper cites.
Pixel recurrent neural networks
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Practical coreset constructions for machine learning
Olivier Bachem, Mario Lucic, and Andreas Krause · 2017
Earlier work this paper cites.
Privacy-preserving generative deep neural networks support clinical data sharing. biorxiv
Brett K Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, and Casey S Greene · 2017
Earlier work this paper cites.
Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Earlier work this paper cites.
Logan: Membership inference attacks against generative models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2017
Earlier work this paper cites.
Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabás Póczos · 2017
Earlier work this paper cites.
Rényi differential privacy
Ilya Mironov · 2017
Earlier work this paper cites.
Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
Earlier work this paper cites.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
Earlier work this paper cites.
Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
Earlier work this paper cites.
Differentially private mixture of generative neural networks
Gergely Acs, Luca Melis, Claude Castelluccia, and Emiliano De Cristofaro · 2018
Earlier work this paper cites.
Demystifying MMD GANs
Mikołaj Bińkowski, Dougal J. Sutherland, Michael Arbel, and Arthur Gretton · 2018
Earlier work this paper cites.
Differentially private data generative models
Qingrong Chen, Chong Xiang, Minhui Xue, Bo Li, Nikita Borisov, Dali Kaarfar, and Haojin Zhu · 2018
Cited alongside, same era.
Learning generative models with sinkhorn divergences
Aude Genevay, Gabriel Peyré, and Marco Cuturi · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Are gans created equal? a large-scale study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2018
Cited alongside, same era.
Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Ulfar Erlingsson · 2018
Cited alongside, same era.
Iterative methods for private synthetic data: Unifying framework and new methods
Terrance Liu, Giuseppe Vietri, and Steven Z Wu · 2021
Later among the works it cites.
G-pate: Scalable differentially private data generator via private aggregation of teacher discriminators
Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl Gunter, and Bo Li · 2021
Later among the works it cites.
Differentially private sliced wasserstein distance
Alain Rakotomamonjy and Ralaivola Liva · 2021
Later among the works it cites.
P3gm: Private high-dimensional data release via privacy preserving phased generative model
Shun Takagi, Tsubasa Takahashi, Yang Cao, and Masatoshi Yoshikawa · 2021
Later among the works it cites.
Differentially private synthetic mixed-type data generation for unsupervised learning
Uthaipon Tao Tantipongpipat, Chris Waites, Digvijay Boob, Amaresh Ankit Siva, and Rachel Cummings · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Aleksei Triastcyn and Boi Faltings · 2018
Cited alongside, same era.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros · 2018
Cited alongside, same era.
Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
Cited alongside, same era.
Differentially private releasing via deep generative model (technical report)
Xinyang Zhang, Shouling Ji, and Ting Wang · 2018
Cited alongside, same era.
Privacy preserving synthetic data release using deep learning
Nazmiye Ceren Abay, Yan Zhou, Murat Kantarcioglu, Bhavani Thuraisingham, and Latanya Sweeney · 2019
Cited alongside, same era.
Differential Privacy Synthetic Data Generation using WGANs, 2019
Moustafa Alzantot and Mani Srivastava · 2019
Cited alongside, same era.
Generative models for effective ml on private, decentralized datasets
Sean Augenstein, H Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, and Blaise Aguera y Arcas · 2019
Cited alongside, same era.
Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau · 2021
Later among the works it cites.
Differentially private learning needs better features (or much more data)
Florian Tramer and Dan Boneh · 2021
Later among the works it cites.
Differentially private normalizing flows for privacy-preserving density estimation
Chris Waites and Rachel Cummings · 2021
Later among the works it cites.
Datalens: Scalable privacy preserving training via gradient compression and aggregation
Boxin Wang, Fan Wu, Yunhui Long, Luka Rimanic, Ce Zhang, and Bo Li · 2021
Later among the works it cites.
Feddpgan: federated differentially private generative adversarial networks framework for the detection of covid-19 pneumonia
Longling Zhang, Bochen Shen, Ahmed Barnawi, Shan Xi, Neeraj Kumar, and Yi Wu · 2021
Later among the works it cites.
How faithful is your synthetic data? sample-level metrics for evaluating and auditing generative models
Ahmed Alaa, Boris Van Breugel, Evgeny S Saveliev, and Mihaela van der Schaar · 2022
Later among the works it cites.
Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
Later among the works it cites.
Differentially private diffusion models
Tim Dockhorn, Tianshi Cao, Arash Vahdat, and Karsten Kreis · 2022
Later among the works it cites.
Differentially private data generation needs better features
Fredrik Harder, Milad Jalali Asadabadi, Danica J Sutherland, and Mijung Park · 2022
Later among the works it cites.
Tapas: a toolbox for adversarial privacy auditing of synthetic data
Florimond Houssiau, James Jordon, Samuel N Cohen, Owen Daniel, Andrew Elliott, James Geddes, Callum Mole, Camila Rangel-Smith, and Lukasz Szpruch · 2022
Later among the works it cites.
Synthetic data–what, why and how?
James Jordon, Lukasz Szpruch, Florimond Houssiau, Mirko Bottarelli, Giovanni Cherubin, Carsten Maple, Samuel N Cohen, and Adrian Weller · 2022
Later among the works it cites.
Bjarne Pfitzner and Bert Arnrich · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Bayesian pseudo posterior mechanism under asymptotic differential privacy
Terrance D Savitsky, Matthew R Williams, and Jingchen Hu · 2022
Later among the works it cites.
Why the search for a privacy-preserving data sharing mechanism is failing
Theresa Stadler and Carmela Troncoso · 2022
Later among the works it cites.
Synthetic data – anonymisation groundhog day
Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso · 2022
Later among the works it cites.
Considerations for differentially private learning with large-scale public pretraining
Florian Tramèr, Gautam Kamath, and Nicholas Carlini · 2022
Later among the works it cites.
Hermite polynomial features for private data generation
Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, and Mi Jung Park · 2022
Later among the works it cites.
Alex Bie, Gautam Kamath, and Guojun Zhang · 2023
Closest in time.
What is synthetic data? the good, the bad, and the ugly
Emiliano De Cristofaro · 2023
Closest in time.
Differentially private diffusion models generate useful synthetic images
Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal, Ira Ktena, Robert Stanforth, Jamie Hayes, Soham De, Samuel L Smith, Olivia Wiles, and Borja Balle · 2023
Closest in time.
Sok: Privacy-preserving data synthesis
Yuzheng Hu, Fan Wu, Qinbin Li, Yunhui Long, Gonzalo Munilla Garrido, Chang Ge, Bolin Ding, David Forsyth, Bo Li, and Dawn Song · 2023
Closest in time.
Dp-lflow: Differentially private latent flow for scalable sensitive image generation
Dihong Jiang and Sun Sun · 2023
Closest in time.
Differentially private synthetic data via foundation model apis 1: Images
Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, and Sergey Yekhanin · 2023
Closest in time.
Differentially private latent diffusion models
Saiyue Lyu, Margarita Vinaroz, Michael F Liu, and Mijung Park · 2023
Closest in time.
Tight auditing of differentially private machine learning
Milad Nasr, Jamie Hayes, Thomas Steinke, Borja Balle, Florian Tramèr, Matthew Jagielski, Nicholas Carlini, and Andreas Terzis · 2023
Closest in time.
How to dp-fy ml: A practical guide to machine learning with differential privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Thakurta · 2023
Closest in time.
Fed-gloss-dp: Federated, global learning using synthetic sets with record level differential privacy
Hui-Po Wang, Dingfan Chen, Raouf Kerkouche, and Mario Fritz · 2023
Closest in time.
Feddm: Iterative distribution matching for communication-efficient federated learning
Yuanhao Xiong, Ruochen Wang, Minhao Cheng, Felix Yu, and Cho-Jui Hsieh · 2023
Closest in time.
Differentially private neural tangent kernels for privacy-preserving data generation
Yilin Yang, Kamil Adamczewski, Danica J Sutherland, Xiaoxiao Li, and Mijung Park · 2023
Closest in time.