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Although machine learning models trained on massive data have led to break-throughs in several areas, their deployment in privacy-sensitive domains remains limited due to restricted access to data.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE
1998
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of cryptography conference
2006
Earlier work this paper cites.
K. Chaudhuri and S. A. Vinterbo, “A stability-based validation procedure for differentially private machine learning,” in Advances in Neural Information Processing Systems 26
2013
Earlier work this paper cites.
M. Cuturi, “Sinkhorn distances: Lightspeed computation of optimal transport,” in Advances in neural information processing systems
2013
Earlier work this paper cites.
C. Dwork and A. Roth, “The Algorithmic Foundations of Differential Privacy,” Found. Trends Theor. Comput. Sci
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems 27
2014
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in Proceedings of International Conference on Computer Vision (ICCV)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings
2015
Earlier work this paper cites.
C. A. Gomez-Uribe and N. Hunt, “The netflix recommender system: Algorithms, business value, and innovation,” ACM Trans. Manage. Inf. Syst
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security
2016
Earlier work this paper cites.
M. Arjovsky and L. Bottou, “Towards Principled Methods for Training Generative Adversarial Networks,” in International Conference on Learning Representations
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
N. Papernot, M. Abadi, Úlfar Erlingsson, I. Goodfellow, and K. Talwar, “Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data,” in International Conference on Learning Representations
2017
Earlier work this paper cites.
A. Sarwate, “Retraction for symmetric matrix perturbation for differentially-private principal component analysis,” 2017
2017
Earlier work this paper cites.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” arXiv preprint arXiv:1701.07875
2017
Earlier work this paper cites.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” in Advances in neural information processing systems
2017
Earlier work this paper cites.
I. Mironov, “Rényi differential privacy,” in 2017 IEEE 30th Computer Security Foundations Symposium (CSF)
2017
Earlier work this paper cites.
L. Mescheder, S. Nowozin, and A. Geiger, “The numerics of gans,” in Advances in Neural Information Processing Systems
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” in Advances in neural information processing systems
2017
Cited alongside, same era.
2018
Cited alongside, same era.
L. Mescheder, A. Geiger, and S. Nowozin, “Which training methods for GANs do actually converge?,” in International Conference on Machine Learning
2018
Cited alongside, same era.
B. Poole, S. Ozair, A. Van Den Oord, A. Alemi, and G. Tucker, “On variational bounds of mutual information,” in International Conference on Machine Learning
2019
Later among the works it cites.
B. K. Beaulieu-Jones, Z. S. Wu, C. Williams, R. Lee, S. P. Bhavnani, J. B. Byrd, and C. S. Greene, “Privacy-preserving generative deep neural networks support clinical data sharing,” Circulation: Cardiovascular Quality and Outcomes
2019
Later among the works it cites.
R. Cummings, V. Gupta, D. Kimpara, and J. Morgenstern, “On the compatibility of privacy and fairness,” in Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization
2019
Later among the works it cites.
2019
Later among the works it cites.
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J. Jordon, J. Yoon, and M. van der Schaar, “Pate-gan: Generating synthetic data with differential privacy guarantees,” in International Conference on Learning Representations
2018
Cited alongside, same era.
G. Acs, L. Melis, C. Castelluccia, and E. De Cristofaro, “Differentially private mixture of generative neural networks,” IEEE Transactions on Knowledge and Data Engineering
2018
Cited alongside, same era.
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida, “Spectral normalization for generative adversarial networks,” in International Conference on Learning Representations
2018
Cited alongside, same era.
B. Balle, G. Barthe, and M. Gaboardi, “Privacy amplification by subsampling: Tight analyses via couplings and divergences,” in Advances in Neural Information Processing Systems
2018
Cited alongside, same era.
T. Salimans, H. Zhang, A. Radford, and D. Metaxas, “Improving GANs using optimal transport,” in International Conference on Learning Representations
2018
Cited alongside, same era.
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro, “Logan: Membership inference attacks against generative models,” in Proceedings on Privacy Enhancing Technologies (PoPETs)
2019
Cited alongside, same era.
L. Frigerio, A. S. de Oliveira, L. Gomez, and P. Duverger, “Differentially private generative adversarial networks for time series, continuous, and discrete open data,” in IFIP International Conference on ICT Systems Security and Privacy Protection
2019
Cited alongside, same era.
J. Yoon, J. Jordon, and M. van der Schaar, “PATE-GAN: Generating synthetic data with differential privacy guarantees,” in International Conference on Learning Representations
2019
Cited alongside, same era.
A. Grover, J. Song, A. Kapoor, K. Tran, A. Agarwal, E. J. Horvitz, and S. Ermon, “Bias correction of learned generative models using likelihood-free importance weighting,” in Advances in Neural Information Processing Systems
2019
Later among the works it cites.
D. Ho, S. R. Quake, E. R. B. McCabe, W. J. Chng, E. K. Chow, X. Ding, B. D. Gelb, G. S. Ginsburg, J. Hassenstab, C.-M. Ho, W. C. Mobley, G. P. Nolan, S. T. Rosen, P. Tan, Y. Yen, and A. Zarrinpar, “Enabling Technologies for Personalized and Precision Medicine,” Trends Biotechnol
2020
Later among the works it cites.
M. Wang and W. Deng, “Deep Face Recognition: A Survey,” arXiv preprint arXiv:1804.06655
2020
Later among the works it cites.
S. Augenstein, H. B. McMahan, D. Ramage, S. Ramaswamy, P. Kairouz, M. Chen, R. Mathews, and B. A. y Arcas, “Generative Models for Effective ML on Private, Decentralized Datasets,” in International Conference on Learning Representations
2020
Later among the works it cites.
D. Chen, T. Orekondy, and M. Fritz, “GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators,” in Advances in Neural Information Processing Systems
2020
Later among the works it cites.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
PhD thesis, University of Waterloo, 2020
S. Agarwal, Trade-Offs between Fairness, Interpretability, and Privacy in Machine Learning · 2020
Later among the works it cites.
K. Choi, A. Grover, T. Singh, R. Shu, and S. Ermon, “Fair generative modeling via weak supervision,” in Proceedings of the 37th International Conference on Machine Learning
2020
Later among the works it cites.
N. Yu, K. Li, P. Zhou, J. Malik, L. Davis, and M. Fritz, “Inclusive GAN: improving data and minority coverage in generative models,” in Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXII
2020
Later among the works it cites.
PhD thesis, ENS, Mar 2020
J. Feydy, Geometric data analysis, beyond convolutions · 2020
Later among the works it cites.
2021
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2021
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