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This paper considers the generalization performance of differentially private convex learning.
Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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Bayesian learning via stochastic gradient Langevin dynamics
Max Welling and Yee W Teh · 2011
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
Ohad Shamir and Tong Zhang · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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
Cited alongside, same era.
Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Ben Recht, and Yoram Singer · 2016
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
Cited alongside, same era.
Private stochastic convex optimization with optimal rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, and Abhradeep Thakurta · 2019
Cited alongside, same era.
Making the last iterate of SGD information theoretically optimal
Prateek Jain, Dheeraj Nagaraj, and Praneeth Netrapalli · 2019
Later among the works it cites.
Stability of stochastic gradient descent on nonsmooth convex losses
R. Bassily, V. Feldman, C. Guzmán, and K. Talwar · 2020
Later among the works it cites.
Private stochastic convex optimization: optimal rates in linear time
Vitaly Feldman, Tomer Koren, and Kunal Talwar · 2020
Later among the works it cites.
Yoav Freund, Yi-An Ma, and Tong Zhang · 2021
Later among the works it cites.
Evading curse of dimensionality in unconstrained private glms via private gradient descent
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta · 2021
Later among the works it cites.
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