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We study stochastic convex optimization with heavy-tailed data under the constraint of differential privacy (DP).
Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Privacy-preserving logistic regression
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Cynthia Dwork, Guy N. Rothblum, and Salil Vadhan · 2010
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Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2011
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A variant of azuma’s inequality for martingales with subgaussian tails
Ohad Shamir · 2011
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Private convex empirical risk minimization and high-dimensional regression
Daniel Kifer, Adam Smith, and Abhradeep Thakurta · 2012
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Learning in a large function space: Privacy-preserving mechanisms for SVM learning
Benjamin Rubinstein, Peter Bartlett, Ling Huang, and Nina Taft · 2012
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Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
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Differentially private feature selection via stability arguments, and the robustness of the lasso
Abhradeep Guha Thakurta and Adam Smith · 2013
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Privacy and statistical risk: Formalisms and minimax bounds
Rina Foygel Barber and John C Duchi · 2014
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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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Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 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
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Convex optimization: Algorithms and complexity
Sébastien Bubeck · 2015
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Robust traceability from trace amounts
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 2015
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Interactive fingerprinting codes and the hardness of preventing false discovery
Thomas Steinke and Jonathan Ullman · 2015
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Nearly-optimal private LASSO
Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2015
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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
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Robust descent using smoothed multiplicative noise
Matthew J Holland · 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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Efficient privacy-preserving stochastic nonconvex optimization
Lingxiao Wang, Bargav Jayaraman, David Evans, and Quanquan Gu · 2019
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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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Private mean estimation of heavy-tailed distributions
Gautam Kamath, Vikrant Singhal, and Jonathan Ullman · 2020
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On differentially private stochastic convex optimization with heavy-tailed data
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Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Concentrated differential privacy
Cynthia Dwork and Guy N. Rothblum · 2016
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Efficient private empirical risk minimization for high-dimensional learning
Shiva Prasad Kasiviswanathan and Hongxia Jin · 2016
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Bolt-on differential privacy for scalable stochastic gradient descent-based analytics
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, and Jeffrey Naughton · 2017
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Differentially private empirical risk minimization revisited: Faster and more general
Di Wang, Minwei Ye, and Jinhui Xu · 2017
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cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan · 2018
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Di Wang, Hanshen Xiao, Srinivas Devadas, and Jinhui Xu · 2020
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Private stochastic convex optimization: Optimal rates in ℓ 1 \ell_{1} geometry
Hilal Asi, Vitaly Feldman, Tomer Koren, and Kunal Talwar · 2021
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Differentially private assouad, fano, and le cam
Jayadev Acharya, Ziteng Sun, and Huanyu Zhang · 2021
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Non-euclidean differentially private stochastic convex optimization
Raef Bassily, Cristóbal Guzmán, and Anupama Nandi · 2021
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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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Estimating smooth glm in non-interactive local differential privacy model with public unlabeled data
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Wide network learning with differential privacy
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High dimensional differentially private stochastic optimization with heavy-tailed data
Lijie Hu, Shuo Ni, Hanshen Xiao, and Di Wang · 2022
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