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The Private Aggregation of Teacher Ensembles (PATE) framework is one of the most promising recent approaches in differentially private learning.
The nature of statistical learning theory
Vladimir N Vapnik · 1995
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Smooth discrimination analysis
Enno Mammen and Alexandre B Tsybakov · 1999
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Introduction to statistical learning theory
Olivier Bousquet, Stéphane Boucheron, and Gábor Lugosi · 2004
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Optimal aggregation of classifiers in statistical learning
Alexander B Tsybakov · 2004
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Theory of classification: A survey of some recent advances
Stéphane Boucheron, Olivier Bousquet, and Gábor Lugosi · 2005
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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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Privacy: Theory meets practice on the map
Ashwin Machanavajjhala, Daniel Kifer, John Abowd, Johannes Gehrke, and Lars Vilhuber · 2008
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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A multiplicative weights mechanism for privacy-preserving data analysis
Moritz Hardt and Guy N Rothblum · 2010
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Learnability, stability and uniform convergence
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2010
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Sample complexity bounds for differentially private learning
Kamalika Chaudhuri and Daniel Hsu · 2011
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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Characterizing the sample complexity of private learners
Amos Beimel, Kobbi Nissim, and Uri Stemmer · 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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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 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
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
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Learning with differential privacy: Stability, learnability and the sufficiency and necessity of erm principle
Yu-Xiang Wang, Jing Lei, and Stephen E. Fienberg · 2016
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
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Improving gaussian mechanism for differential privacy: Analytical calibration and optimal denoising
Borja Balle and Yu-Xiang Wang · 2018
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Privacy-preserving prediction
Cynthia Dwork and Vitaly Feldman · 2018
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Learning differentially private recurrent language models
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Theory of disagreement-based active learning
Steve Hanneke · 2014
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Beyond disagreement-based agnostic active learning
Chicheng Zhang and Kamalika Chaudhuri · 2014
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Differentially private release and learning of threshold functions
Mark Bun, Kobbi Nissim, Uri Stemmer, and Salil Vadhan · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Privacy for free: Posterior sampling and stochastic gradient monte carlo
Yu-Xiang Wang, Stephen Fienberg, and Alex Smola · 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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H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
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Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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Active learning with logged data
Songbai Yan, Kamalika Chaudhuri, and Tara Javidi · 2018
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Limits of private learning with access to public data
Noga Alon, Raef Bassily, and Shay Moran · 2019
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Subsampled rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Kasiviswanathan · 2019
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Improving differentially private models with active learning
Zhengli Zhao, Nicolas Papernot, Sameer Singh, Neoklis Polyzotis, and Augustus Odena · 2019
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Pac learning with stable and private predictions
Yuval Dagan and Vitaly Feldman · 2020
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Privately answering classification queries in the agnostic pac model
Anupama Nandi and Raef Bassily · 2020
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