Fetching the paper…
Reading the bibliography…
In this paper we study the differentially private Empirical Risk Minimization (ERM) problem in different settings.
Gradient methods for the minimisation of functionals
B. T. Polyak · 1963
Earlier work this paper cites.
Smooth minimization of non-smooth functions
Y. Nesterov · 2005
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
Earlier work this paper cites.
Cubic regularization of newton method and its global performance
Y. Nesterov and B. T. Polyak · 2006
Earlier work this paper cites.
Privacy-preserving logistic regression
K. Chaudhuri and C. Monteleoni · 2009
Earlier work this paper cites.
Private coresets
D. Feldman, A. Fiat, H. Kaplan, and K. Nissim · 2009
Earlier work this paper cites.
Boosting and differential privacy
C. Dwork, G. N. Rothblum, and S. Vadhan · 2010
Earlier work this paper cites.
Differentially private empirical risk minimization
K. Chaudhuri, C. Monteleoni, and A. D. Sarwate · 2011
Earlier work this paper cites.
Near-optimal differentially private principal components
K. Chaudhuri, A. Sarwate, and K. Sinha · 2012
Earlier work this paper cites.
Differentially private online learning
P. Jain, P. Kothari, and A. Thakurta · 2012
Earlier work this paper cites.
Private convex empirical risk minimization and high-dimensional regression
D. Kifer, A. Smith, and A. Thakurta · 2012
Earlier work this paper cites.
Beyond worst-case analysis in private singular vector computation
M. Hardt and A. Roth · 2013
Earlier work this paper cites.
(nearly) optimal algorithms for private online learning in full-information and bandit settings
A. G. Thakurta and A. Smith · 2013
Cited alongside, same era.
Private empirical risk minimization: Efficient algorithms and tight error bounds
R. Bassily, A. Smith, and A. Thakurta · 2014
Cited alongside, same era.
Analyze gauss: optimal bounds for privacy-preserving principal component analysis
C. Dwork, K. Talwar, A. Thakurta, and L. Zhang · 2014
Cited alongside, same era.
Stochastic proximal gradient descent with acceleration techniques
A. Nitanda · 2014
Cited alongside, same era.
Private empirical risk minimization beyond the worst case: The effect of the constraint set geometry
K. Talwar, A. Thakurta, and L. Zhang · 2014
Cited alongside, same era.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
M. Bun and T. Steinke · 2016
Later among the works it cites.
Linear convergence of gradient and proximal-gradient methods under the polyak-łojasiewicz condition
H. Karimi, J. Nutini, and M. Schmidt · 2016
Later among the works it cites.
Efficient private empirical risk minimization for high-dimensional learning
S. P. Kasiviswanathan and H. Jin · 2016
Later among the works it cites.
G. Li and T. K. Pong · 2016
Later among the works it cites.
Stochastic variance reduction for nonconvex optimization
S. J. Reddi, A. Hefny, S. Sra, B. Poczos, and A. Smola · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
L. Xiao and T. Zhang · 2014
Cited alongside, same era.
Beyond convexity: Stochastic quasi-convex optimization
E. Hazan, K. Levy, and S. Shalev-Shwartz · 2015
Cited alongside, same era.
Nearly optimal private lasso
K. Talwar, A. Thakurta, and L. Zhang · 2015
Cited alongside, same era.
Revisiting differentially private regression: Lessons from learning theory and their consequences
X. Wu, M. Fredrikson, W. Wu, S. Jha, and J. F. Naughton · 2015
Cited alongside, same era.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Cited alongside, same era.
Improved SVRG for Non-Strongly-Convex or Sum-of-Non-Convex Objectives
Z. Allen-Zhu and Y. Yuan · 2016
Cited alongside, same era.
Learning with differential privacy: Stability, learnability and the sufficiency and necessity of erm principle
Y.-X. Wang, J. Lei, and S. E. Fienberg · 2016
Later among the works it cites.
The price of differential privacy for online learning
N. Agarwal and K. Singh · 2017
Later among the works it cites.
Katyusha: the first direct acceleration of stochastic gradient methods
Z. Allen-Zhu · 2017
Later among the works it cites.
Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent
Z. Allen-Zhu and L. Orecchia · 2017
Later among the works it cites.
Private incremental regression
S. P. Kasiviswanathan, K. Nissim, and H. Jin · 2017
Later among the works it cites.
Efficient private erm for smooth objectives
J. Zhang, K. Zheng, W. Mou, and L. Wang · 2017
Later among the works it cites.