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An algorithm for the machine calculation of complex fourier series
James W Cooley and John W Tukey · 1965
Earlier work this paper cites.
High-speed convolution and correlation
Thomas G Stockham Jr · 1966
Earlier work this paper cites.
When random sampling preserves privacy
Kamalika Chaudhuri and Nina Mishra · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil Vadhan · 2010
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
Earlier work this paper cites.
A differentially private stochastic gradient descent algorithm for multiparty classification
Arun Rajkumar and Shivani Agarwal · 2012
Earlier work this paper cites.
Characterizing the sample complexity of private learners
Amos Beimel, Kobbi Nissim, and Uri Stemmer · 2013
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate · 2013
Cited alongside, same era.
Introduction to numerical analysis
Josef Stoer and Roland Bulirsch · 2013
Cited alongside, same era.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
Privacy for free: Posterior sampling and stochastic gradient Monte Carlo
Yu-Xiang Wang, Stephen E. Fienberg, and Alexander J. Smola · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Rényi differential privacy
Ilya Mironov · 2017
Later among the works it cites.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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Tight on budget?: Tight bounds for r-fold approximate differential privacy
Sebastian Meiser and Esfandiar Mohammadi · 2018
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Rényi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang · 2019
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Privacy loss classes: The central limit theorem in differential privacy
David M Sommer, Sebastian Meiser, and Esfandiar Mohammadi · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
Martin J Wainwright · 2019
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Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2017
Cited alongside, same era.
Closest in time.
Subsampled Rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Kasiviswanathan · 2019
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Poission subsampled Rényi differential privacy
Yuqing Zhu and Yu-Xiang Wang · 2019
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