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Differential privacy (DP) is a popular mechanism for training machine learning models with bounded leakage about the presence of specific points in the training data.
Toward efficient agnostic learning
M. J. Kearns, R. E. Schapire, and L. M. Sellie · 1994
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SMOTE: Synthetic minority over-sampling technique
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer · 2002
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Differential privacy
C. Dwork · 2011
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A firm foundation for private data analysis
C. Dwork · 2011
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On the difficulty of training recurrent neural networks
R. Pascanu, T. Mikolov, and Y. Bengio · 2013
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GloVe: Global vectors for word representation
J. Pennington, R. Socher, and C. Manning · 2014
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Adding gradient noise improves learning for very deep networks
A. Neelakantan, L. Vilnis, Q. V. Le, I. Sutskever, L. Kaiser, K. Kurach, and J. Martens · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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Demographic dialectal variation in social media: A case study of African-American English
S. L. Blodgett, L. Green, and B. O’Connor · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Data decisions and theoretical implications when adversarially learning fair representations
A. Beutel, J. Chen, Z. Zhao, and E. H. Chi · 2017
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Communication-efficient learning of deep networks from decentralized data
H. B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Agüera y Arcas · 2017
Cited alongside, same era.
Rényi differential privacy
I. Mironov · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Cited alongside, same era.
Age progression/regression by conditional adversarial autoencoder
Z. Zhang, Y. Song, and H. Qi · 2017
Cited alongside, same era.
A reductions approach to fair classification
A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. Wallach · 2018
Cited alongside, same era.
Twitter universal dependency parsing for African-American and mainstream American English
S. L. Blodgett, J. Wei, and B. O’Connor · 2018
Cited alongside, same era.
A general approach to adding differential privacy to iterative training procedures
H. B. McMahan, G. Andrew, Ú. Erlingsson, S. Chien, I. Mironov, N. Papernot, and P. Kairouz · 2018
Later among the works it cites.
Learning differentially private recurrent language models
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang · 2018
Later among the works it cites.
Privacy risk in machine learning: Analyzing the connection to overfitting
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha · 2018
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Class-balanced loss based on effective number of samples
Y. Cui, M. Jia, T.-Y. Lin, Y. Song, and S. J. Belongie · 2019
Closest in time.
On the compatibility of privacy and fairness
R. Cummings, V. Gupta, D. Kimpara, and J. Morgenstern · 2019
Closest in time.
Differentially private fair learning
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A systematic study of the class imbalance problem in convolutional neural networks
M. Buda, A. Maki, and M. A. Mazurowski · 2018
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
Cited alongside, same era.
Effective data generation for imbalanced learning using conditional generative adversarial networks
G. Douzas and F. Bacao · 2018
Cited alongside, same era.
Adversarial removal of demographic attributes from text data
Y. Elazar and Y. Goldberg · 2018
Cited alongside, same era.
Differentially private federated learning: A client level perspective
R. C. Geyer, T. Klein, and M. Nabi · 2018
Cited alongside, same era.
The iNaturalist species classification and detection dataset
G. V. Horn, O. M. Aodha, Y. Song, Y. Cui, C. Sun, A. Shepard, H. Adam, P. Perona, and S. Belongie · 2018
Cited alongside, same era.
M. Jagielski, M. Kearns, J. Mao, A. Oprea, A. Roth, S. Sharifi-Malvajerdi, and J. Ullman · 2019
Closest in time.
Fair decision making using privacy-protected data
S. Kuppam, R. Mckenna, D. Pujol, M. Hay, A. Machanavajjhala, and G. Miklau · 2019
Closest in time.
M. Merler, N. Ratha, R. S. Feris, and J. R. Smith · 2019
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Agnostic federated learning
M. Mohri, G. Sivek, and A. T. Suresh · 2019
Closest in time.
[Online; accessed 14-May-2019]
https://github.com/pytorch/ , 2019 · 2019
Closest in time.
[Online; accessed 14-May-2019]
https://github.com/tensorflow/privacy , 2019 · 2019
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Disparate vulnerability: On the unfairness of privacy attacks against machine learning
M. Yaghini, B. Kulynych, and C. Troncoso · 2019
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Federated heavy hitters discovery with differential privacy
W. Zhu, P. Kairouz, H. Sun, B. McMahan, and W. Li · 2019
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