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In this work, we study how to use sampling to speed up mechanisms for answering adaptive queries into datasets without reducing the accuracy of those mechanisms.
An efficient method for weighted sampling without replacement
C. K. Wong and Malcolm C. Easton · 1980
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Faster methods for random sampling
Jeffrey Scott Vitter · 1984
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Weakly learning DNF and characterizing statistical query learning using Fourier analysis
Avrim Blum, Merrick L. Furst, Jeffrey C. Jackson, Michael J. Kearns, Yishay Mansour, and Steven Rudich · 1994
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Efficient noise-tolerant learning from statistical queries
Michael J. Kearns · 1998
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On learning correlated Boolean functions using statistical queries
Ke Yang · 2001
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The complexity of massive data set computations
Ziv Bar-Yossef · 2002
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam D. Smith · 2006
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A learning theory approach to non-interactive database privacy
Avrim Blum, Katrina Ligett, and Aaron Roth · 2008
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith · 2008
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Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 2010
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Adaptive bootstrapping of recommender systems using decision trees
Nadav Golbandi, Yehuda Koren, and Ronny Lempel · 2011
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The big data bootstrap
Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, and Michael I. Jordan · 2012
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First-order methods with inexact oracle: the strongly convex case
Olivier Devolder, François Glineur, and Yurii Nesterov · 2013
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Practical differential privacy via grouping and smoothing
Georgios Kellaris and Stavros Papadopoulos · 2013
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On the benefits of sampling in privacy preserving statistical analysis on distributed databases
Bing-Rong Lin, Ye Wang, and Shantanu Rane · 2013
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
Ohad Shamir and Tong Zhang · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
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Conservative or liberal? Personalized differential privacy
Zach Jorgensen, Ting Yu, and Graham Cormode · 2015
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Between pure and approximate differential privacy
Thomas Steinke and Jon Ullman · 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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Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
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Exact post-selection inference, with application to the lasso
Jason D. Lee, Dennis L. Sun, Yuekai Sun, and Jonathan E. Taylor · 2016
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Raef Bassily, Adam D. Smith, and Abhradeep Thakurta · 2014
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Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil P. Vadhan · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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The statistical crisis in science data-dependent analysis—a “garden of forking paths”—explains why many statistically significant comparisons don’t hold up
Andrew Gelman and Eric Loken · 2014
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Differentially private release and learning of threshold functions
Mark Bun, Kobbi Nissim, Uri Stemmer, and Salil P. Vadhan · 2015
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Generalization in adaptive data analysis and holdout reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth · 2015
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Statistical algorithms and a lower bound for detecting planted cliques
Vitaly Feldman, Elena Grigorescu, Lev Reyzin, Santosh Srinivas Vempala, and Ying Xiao
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Max-information, differential privacy, and post-selection hypothesis testing
Ryan M. Rogers, Aaron Roth, Adam D. Smith, and Om Thakkar · 2016
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Towards confidence in the truth: A bootstrapping based truth discovery approach
Houping Xiao, Jing Gao, Qi Li, Fenglong Ma, Lu Su, Yunlong Feng, and Aidong Zhang · 2016
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Statistical query algorithms for mean vector estimation and stochastic convex optimization
Vitaly Feldman, Cristobal Guzman, and Santosh Vempala · 2017
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Privacy amplification by subsampling: Tight analyses via couplings and divergences
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2018
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The limits of post-selection generalization
Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2018
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