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We tackle the problem of estimating a location parameter with differential privacy guarantees and sub-Gaussian deviations.
The notion of breakdown point
David L Donoho and Peter J Huber · 1983
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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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Smooth sensitivity and sampling in private data analysis
Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2007
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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Robust Statistics
Peter J. Huber and Elvezio Ronchetti · 2009
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A statistical framework for differential privacy
Larry Wasserman and Shuheng Zhou · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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Differentially private M-estimators
Jing Lei · 2011
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Privacy-preserving statistical estimation with optimal convergence rates
Adam Smith · 2011
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Challenging the empirical mean and empirical variance: a deviation study
Olivier Catoni · 2012
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Convergence rates for differentially private statistical estimation
Kamalika Chaudhuri and Daniel Hsu · 2012
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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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Bandits with heavy tail
Sébastien Bubeck, Nicolo Cesa-Bianchi, and Gábor Lugosi · 2013
Cited alongside, same era.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
Cited alongside, same era.
(nearly) optimal differentially private stochastic multi-arm bandits
Nikita Mishra and Abhradeep Thakurta · 2015
Cited alongside, same era.
Nearly optimal private lasso
Kunal Talwar, Abhradeep Guha Thakurta, and Li Zhang · 2015
Cited alongside, same era.
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.
Differentially private ordinary least squares
Or Sheffet · 2017
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Robust estimation of high-dimensional covariance and precision matrices
Marco Avella-Medina, Heather S Battey, Jianqing Fan, and Quefeng Li · 2018
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Minimax optimal procedures for locally private estimation
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2018
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Certified robustness to adversarial examples with differential privacy
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana · 2018
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Sub-gaussian estimators of the mean of a random matrix with heavy-tailed entries
Stanislav Minsker · 2018
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Differentially private contextual linear bandits
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Sub-gaussian mean estimators
Luc Devroye, Matthieu Lerasle, Gabor Lugosi, and Roberto I Oliveira · 2016
Cited alongside, same era.
Differentially private chi-squared hypothesis testing: Goodness of fit and independence testing
Marco Gaboardi, Hyun-Woo Lim, Ryan M Rogers, and Salil P Vadhan · 2016
Cited alongside, same era.
Loss minimization and parameter estimation with heavy tails
Daniel Hsu and Sivan Sabato · 2016
Cited alongside, same era.
Inference using noisy degrees: Differentially private beta-model and synthetic graphs
Vishesh Karwa and Aleksandra Slavković · 2016
Cited alongside, same era.
Algorithms for differentially private multi-armed bandits
Aristide CY Tossou and Christos Dimitrakakis · 2016
Cited alongside, same era.
Roshan Shariff and Or Sheffet · 2018
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Privacy-preserving parametric inference: a case for robust statistics
Marco Avella-Medina · 2019
Closest in time.
Differentially private significance tests for regression coefficients
Andrés F Barrientos, Jerome P Reiter, Ashwin Machanavajjhala, and Yan Chen · 2019
Closest in time.
The cost of privacy: optimal rates of convergence for paramer estimaion with differential privacy
Tony T. Cai, Yichen Wang, and Zhang mLinjun · 2019
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Robust machine learning by median-of-means: theory and practice
Guillaume Lecué and Matthieu Lerasle · 2019
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Sub-gaussian estimators of the mean of a random vector
Gábor Lugosi, Shahar Mendelson, et al · 2019
Closest in time.