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We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density.
Convex programming in hilbert space
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Some methods of speeding up the convergence of iteration methods
Boris T. Polyak · 1964
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A method for solving the convex programming problem with convergence rate o ( 1 / k 2 ) o(1/k^{2})
Yu E Nesterov · 1983
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Problem complexity and method efficiency in optimization
Arkadi S. Nemirovski and David B. Yudin · 1983
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The method of inscribed ellipsoids
Leonid G. Khachiyan, Sergei Pavlovich Tarasov, and I. I. Erlikh · 1988
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On parallel complexity of nonsmooth convex optimization
Arkadi Nemirovski · 1994
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Introductory lectures on convex optimization: A basic course
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Online learning: Theory, algorithms, and applications
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Privacy-preserving logistic regression
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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Stochastic gradient descent tricks
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Randomized smoothing for stochastic optimization
John C Duchi, Peter L Bartlett, and Martin J Wainwright · 2012
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Optimal distributed online prediction using mini-batches
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Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization i: A generic algorithmic framework
Saeed Ghadimi and Guanghui Lan · 2012
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Learning in a large function space: Privacy-preserving mechanisms for svm learning
Benjamin IP Rubinstein, Peter L Bartlett, Ling Huang, and Nina Taft · 2012
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An accelerated hybrid proximal extragradient method for convex optimization and its implications to second-order methods
Renato DC Monteiro and Benar Fux Svaiter · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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Convex Optimization
Stephen P. Boyd and Lieven Vandenberghe · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Beyond the regret minimization barrier: optimal algorithms for stochastic strongly-convex optimization
Elad Hazan and Satyen Kale · 2014
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(near) dimension independent risk bounds for differentially private learning
Prateek Jain and Abhradeep Guha Thakurta · 2014
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Understanding Machine Learning - From Theory to Algorithms
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Thomas Steinke and Jonathan Ullman · 2015
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The challenge of scientific reproducibility and privacy protection for statistical agencies
John M. Abowd · 2016
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Complexity of highly parallel non-smooth convex optimization
Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, and Aaron Sidford · 2019
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Variance reduction for matrix games
Yair Carmon, Yujia Jin, Aaron Sidford, and Kevin Tian · 2019
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Lower bounds for parallel and randomized convex optimization
Jelena Diakonikolas and Cristóbal Guzmán · 2019
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Near optimal methods for minimizing convex functions with lipschitz p p -th derivatives
Alexander Gasnikov, Pavel Dvurechensky, Eduard Gorbunov, Evgeniya Vorontsova, Daniil Selikhanovych, César A Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sébastien Bubeck, et al · 2019
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Towards practical differentially private convex optimization
Roger Iyengar, Joseph P Near, Dawn Song, Om Thakkar, Abhradeep Thakurta, and Lun Wang · 2019
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Stability of stochastic gradient descent on nonsmooth convex losses
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Introduction to online convex optimization
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Efficient private empirical risk minimization for high-dimensional learning
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Differentially private empirical risk minimization with input perturbation
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Rényi differential privacy
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Acceleration with a ball optimization oracle
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Private stochastic convex optimization: optimal rates in linear time
Vitaly Feldman, Tomer Koren, and Kunal Talwar · 2020
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An improved cutting plane method for convex optimization, convex-concave games, and its applications
Haotian Jiang, Yin Tat Lee, Zhao Song, and Sam Chiu-wai Wong · 2020
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Stochastic bias-reduced gradient methods
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Private stochastic convex optimization: Optimal rates in l1 geometry
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Thinking inside the ball: Near-optimal minimization of the maximal loss
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Private non-smooth erm and sco in subquadratic steps
Janardhan Kulkarni, Yin Tat Lee, and Daogao Liu · 2021
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Evading the curse of dimensionality in unconstrained private glms
Shuang Song, Thomas Steinke, Om Thakkar, and Abhradeep Thakurta · 2021
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The min-max complexity of distributed stochastic convex optimization with intermittent communication
Blake E Woodworth, Brian Bullins, Ohad Shamir, and Nathan Srebro · 2021
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Distributionally robust optimization via ball oracle acceleration
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Private convex optimization via exponential mechanism
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Semi-random sparse recovery in nearly-linear time
Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, and Kevin Tian · 2022
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When does differentially private learning not suffer in high dimensions?
Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A Inan, Janardhan Kulkarni, Yin Tat Lee, and Abhradeep Guha Thakurta · 2022
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Differential privacy and the secrecy of the sample
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Ppml workshop talk: Open questions in differentially private machine learning
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