Fetching the paper…
Reading the bibliography…
We study the problem of approximating stationary points of Lipschitz and smooth functions under $(\varepsilon,\delta)$-differential privacy (DP) in both the finite-sum and stochastic settings.
Problem complexity and method efficiency in optimization
Arkadij Semenovic Nemirovsky and David Borisovich Yudin · 1983
Earlier work this paper cites.
Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
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.
Cubic regularization of newton method and its global performance
Yurii Nesterov and Boris Polyak · 2006
Earlier work this paper cites.
Non-asymptotic theory of random matrices: extreme singular values
Mark Rudelson and Roman Vershynin · 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.
Differentially private online learning
Prateek Jain, Pravesh Kothari, and Abhradeep Thakurta · 2012
Earlier work this paper cites.
Private convex empirical risk minimization and high-dimensional regression
Daniel Kifer, Adam Smith, and Abhradeep Thakurta · 2012
Earlier work this paper cites.
How to make the gradients small
Yurii Nesterov · 2012
Earlier work this paper cites.
Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Saeed Ghadimi and Guanghui Lan · 2013
Earlier work this paper cites.
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 and Aaron Roth · 2014
Earlier work this paper cites.
(near) dimension independent risk bounds for differentially private learning
Prateek Jain and Abhradeep Thakurta · 2014
Earlier work this paper cites.
Private empirical risk minimization beyond the worst case: The effect of the constraint set geometry
Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2014
Earlier work this paper cites.
Between pure and approximate differential privacy
Thomas Steinke and Jonathan Ullman · 2015
Earlier work this paper cites.
Nearly optimal private lasso
Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2015
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
Earlier work this paper cites.
Nearly tight oblivious subspace embeddings by trace inequalities
Michael B Cohen · 2016
Cited alongside, same era.
Lecture notes for statistics 311/electrical engineering 377
John Duchi · 2016
Cited alongside, same era.
Accelerated gradient methods for nonconvex nonlinear and stochastic programming
Saeed Ghadimi and Guanghui Lan · 2016
Cited alongside, same era.
Matrix completion has no spurious local minimum
Rong Ge, Jason D Lee, and Tengyu Ma · 2016
Cited alongside, same era.
A geometric analysis of phase retrieval
Ju Sun, Qing Qu, and John Wright · 2016
Cited alongside, same era.
”convex until proven guilty”: Dimension-free acceleration of gradient descent on non-convex functions
Yair Carmon, John C. Duchi, Oliver Hinder, and Aaron Sidford · 2017
Cited alongside, same era.
A short note on concentration inequalities for random vectors with subgaussian norm
Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M Kakade, and Michael I Jordan · 2019
Later among the works it cites.
Differentially private empirical risk minimization with non-convex loss functions
Di Wang, Changyou Chen, and Jinhui Xu · 2019
Later among the works it cites.
Spiderboost and momentum: Faster variance reduction algorithms
Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, and Vahid Tarokh · 2019
Later among the works it cites.
Differentially private empirical risk minimization with smooth non-convex loss functions: A non-stationary view
Di Wang and Jinhui Xu · 2019
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.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Differentially private empirical risk minimization revisited: Faster and more general
Di Wang, Minwei Ye, and Jinhui Xu · 2017
Cited alongside, same era.
Efficient private erm for smooth objectives
Jiaqi Zhang, Kai Zheng, Wenlong Mou, and Liwei Wang · 2017
Cited alongside, same era.
How to make the gradients small stochastically: Even faster convex and nonconvex sgd
Zeyuan Allen-Zhu · 2018
Cited alongside, same era.
Composable and versatile privacy via truncated cdp
Mark Bun, Cynthia Dwork, Guy N. Rothblum, and Thomas Steinke · 2018
Cited alongside, same era.
Spider: Near-optimal non-convex optimization via stochastic path-integrated differential estimator
Cong Fang, Chris Junchi Li, Zhouchen Lin, and Tong Zhang · 2018
Cited alongside, same era.
Uniform convergence of gradients for non-convex learning and optimization
Dylan J Foster, Ayush Sekhari, and Karthik Sridharan · 2018
Cited alongside, same era.
Gautam Kamath and Jonathan Ullman · 2020
Later among the works it cites.
First-order and stochastic optimization methods for machine learning
Guanghui Lan · 2020
Later among the works it cites.
Private stochastic non-convex optimization: Adaptive algorithms and tighter generalization bounds
Yingxue Zhou, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, and Arindam Banerjee · 2020
Later among the works it cites.
Private stochastic convex optimization: Optimal rates in l1 geometry
Hilal Asi, Vitaly Feldman, Tomer Koren, and Kunal Talwar · 2021
Later among the works it cites.
Differentially private stochastic optimization: New results in convex and non-convex settings
Raef Bassily, Cristóbal Guzmán, and Michael Menart · 2021
Later among the works it cites.
Non-euclidean differentially private stochastic convex optimization
Raef Bassily, Cristobal Guzman, and Anupama Nandi · 2021
Later among the works it cites.
Private non-smooth erm and sco in subquadratic steps
Janardhan Kulkarni, Yin Tat Lee, and Daogao Liu · 2021
Later among the works it cites.
Evading the curse of dimensionality in unconstrained private glms
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta · 2021
Later among the works it cites.
Private stochastic non-convex optimization with improved utility rates
Qiuchen Zhang, Jing Ma, Jian Lou, and Li Xiong · 2021
Later among the works it cites.
Differentially private generalized linear models revisited
Raman Arora, Raef Bassily, Cristóbal Guzmán, Michael Menart, and Enayat Ullah · 2022
Closest in time.
Complementary composite minimization, small gradients in general norms, and applications, 2023
Jelena Diakonikolas and Cristóbal Guzmán · 2023
Closest in time.