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In this article, we propose a new variational approach to learn private and/or fair representations.
R. W. Yeung, “A new outlook on Shannon’s information measures,” IEEE Transactions on Information Theory , vol. 37, no. 3, pp. 466–474, 1991
1991
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
N. Tishby, F. C. Pereira, and W. Bialek, “The information bottleneck method,” arXiv preprint physics/0004057 , 2000
2000
Earlier work this paper cites.
S. Boyd, S. P. Boyd, and L. Vandenberghe, Convex optimization . Cambridge university press, 2004
2004
Earlier work this paper cites.
Y. Zhu and R. Bettati, “Anonymity vs. information leakage in anonymity systems,” in 25th IEEE International Conference on Distributed Computing Systems (ICDCS’05) . IEEE, 2005, pp. 514–524
2005
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 . Springer, 2006, pp. 265–284
2006
Earlier work this paper cites.
T. M. Cover and J. A. Thomas, Elements of information theory , second edition ed. John Wiley & Sons, 2006
2006
Earlier work this paper cites.
T. Calders, F. Kamiran, and M. Pechenizkiy, “Building classifiers with independency constraints,” in 2009 IEEE International Conference on Data Mining Workshops . IEEE, 2009, pp. 13–18
2009
Earlier work this paper cites.
A. Rahimi and B. Recht, “Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning,” in Advances in neural information processing systems (NeurIPS) , 2009, pp. 1313–1320
2009
Earlier work this paper cites.
K. Chatzikokolakis, T. Chothia, and A. Guha, “Statistical measurement of information leakage,” in International Conference on Tools and Algorithms for the Construction and Analysis of Systems . Springer, 2010, pp. 390–404
2010
Earlier work this paper cites.
F. Kamiran and T. Calders, “Classification with no discrimination by preferential sampling,” in Proceedings of the 19th Machine Learning Conference of Belgium and The Netherlands . Citeseer, 2010, pp. 1–6
2010
Earlier work this paper cites.
D. Kifer and A. Machanavajjhala, “No free lunch in data privacy,” in Proceedings of the 2011 ACM SIGMOD International Conference on Management of data , 2011, pp. 193–204
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research , vol. 12, pp. 2825–2830, 2011
2011
Earlier work this paper cites.
F. du Pin Calmon and N. Fawaz, “Privacy against statistical inference,” in Allerton Conference on Communication, Control, and Computing (Allerton) . IEEE, 2012, pp. 1401–1408
2012
Earlier work this paper cites.
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel, “Fairness through awareness,” in Proceedings of the 3rd Innovations in Theoretical Computer Science conference , 2012, pp. 214–226
2012
Earlier work this paper cites.
R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork, “Learning fair representations,” in International Conference on Machine Learning (ICML) , 2013, pp. 325–333
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
A. Makhdoumi, S. Salamatian, N. Fawaz, and M. Médard, “From the information bottleneck to the privacy funnel,” in IEEE Information Theory Workshop (ITW) . IEEE, 2014, pp. 501–505
2014
Earlier work this paper cites.
C. Dwork, A. Roth et al. , “The algorithmic foundations of differential privacy,” Foundations and Trends® in Theoretical Computer Science , vol. 9, no. 3–4, pp. 211–407, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Cited alongside, same era.
H. Edwards and A. Storkey, “Censoring representations with an adversary,” International Conference on Learning Representations (ICLR) , 2016
2016
Cited alongside, same era.
W. Dieterich, C. Mendoza, and T. Brennan, “Compas risk scales: Demonstrating accuracy equity and predictive parity,” Northpointe Inc , 2016
2016
Cited alongside, same era.
A. A. Alemi, I. Fischer, J. V. Dillon, and K. Murphy, “Deep variational information bottleneck,” International Conference on Learning Representations (ICLR) , 2016
2016
Cited alongside, same era.
C. Louizos, K. Swersky, Y. Li, M. Welling, and R. Zemel, “The variational fair autoencoder,” International Conference on Learning Representations (ICLR) , 2016
R. Nabi and I. Shpitser, “Fair inference on outcomes,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Later among the works it cites.
B. H. Zhang, B. Lemoine, and M. Mitchell, “Mitigating unwanted biases with adversarial learning,” in Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society , 2018, pp. 335–340
2018
Later among the works it cites.
H. Kim and A. Mnih, “Disentangling by factorising,” in International Conference on Machine Learning (ICML) , 2018, pp. 2649–2658
2018
Later among the works it cites.
T. Q. Chen, X. Li, R. B. Grosse, and D. K. Duvenaud, “Isolating sources of disentanglement in variational autoencoders,” in Advances in Neural Information Processing Systems (NeurIPS) , 2018, pp. 2610–2620
2018
Later among the works it cites.
H. Zhao and G. Gordon, “Inherent tradeoffs in learning fair representations,” in Advances in Neural Information Processing Systems (NeurIPS) , 2019, pp. 15 649–15 659
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2016
Cited alongside, same era.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 2096–2030, 2016
2016
Cited alongside, same era.
M. Hardt, E. Price, and N. Srebro, “Equality of opportunity in supervised learning,” in Advances in Neural Information Processing Systems (NeurIPS) , 2016, pp. 3315–3323
2016
Cited alongside, same era.
2016
Cited alongside, same era.
M. B. Zafar, I. Valera, M. Rodriguez Gomez, and K. P. Gummadi, “Fairness constraints: Mechanisms for fair classification,” International Conference on Artificial Intelligence and Statistics (AISTATS) , 2017
2017
Cited alongside, same era.
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner, “Beta-VAE: Learning basic visual concepts with a constrained variational framework.” International Conference on Learning Representations (ICLR) , 2017
2017
Cited alongside, same era.
D. Dua and C. Graff, “UCI machine learning repository,” 2017. [Online]. Available: http://archive.ics.uci.edu/ml
2017
Cited alongside, same era.
J. Hamm, “Minimax filter: learning to preserve privacy from inference attacks,” The Journal of Machine Learning Research , vol. 18, no. 1, pp. 4704–4734, 2017
2017
Cited alongside, same era.
2019
Later among the works it cites.
A. Kolchinsky, B. D. Tracey, and D. H. Wolpert, “Nonlinear information bottleneck,” Entropy , vol. 21, no. 12, p. 1181, 2019
2019
Later among the works it cites.
E. Creager, D. Madras, J.-H. Jacobsen, M. Weis, K. Swersky, T. Pitassi, and R. Zemel, “Flexibly fair representation learning by disentanglement,” in International Conference on Machine Learning (ICML) , 2019, pp. 1436–1445
2019
Later among the works it cites.
I. Issa, A. B. Wagner, and S. Kamath, “An operational approach to information leakage,” IEEE Transactions on Information Theory , vol. 66, no. 3, pp. 1625–1657, 2019
2019
Later among the works it cites.
M. Wainwright, High-Dimensional Statistics . Cambridge University Press, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
R. Jiang, A. Pacchiano, T. Stepleton, H. Jiang, and S. Chiappa, “Wasserstein fair classification,” Conference on Uncertainty in Artificial Intelligence (UAI) , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
D. Madras, E. Creager, T. Pitassi, and R. Zemel, “Fairness through causal awareness: Learning causal latent-variable models for biased data,” in Proceedings of the Conference on Fairness, Accountability, and Transparency , 2019, pp. 349–358
2019
Later among the works it cites.
S. Zhao, J. Song, and S. Ermon, “Infovae: Balancing learning and inference in variational autoencoders,” in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 5885–5892
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” in Advances in neural information processing systems , 2019, pp. 8026–8037
2019
Later among the works it cites.
H. Zhao, A. Coston, T. Adel, and G. J. Gordon, “Conditional learning of fair representations,” International Conference on Learning Representations (ICLR) , 2020
2020
Closest in time.
I. S. Fischer, “The conditional entropy bottleneck,” Entropy , vol. 22, 2020
2020
Closest in time.
L. Nan and D. Tao, “Variational approach for privacy funnel optimization on continuous data,” Journal of Parallel and Distributed Computing , vol. 137, pp. 17–25, 2020
2020
Closest in time.
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith et al. , “Array programming with numpy,” Nature , vol. 585, no. 7825, pp. 357–362, 2020
2020
Closest in time.
B. Rodríguez Gálvez, R. Thobaben, and M. Skoglund, “The convex information bottleneck Lagrangian,” Entropy , vol. 22, no. 1, p. 98, 2020
2020
Closest in time.