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A continuing challenge for machine learning is providing methods to perform computation on data while ensuring the data remains private.
Weaving technology and policy together to maintain confidentiality
Latanya Sweeney · 1997
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Bayesian Gaussian processes for regression and classification
Mark N Gibbs · 1998
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Fast forward selection to speed up sparse Gaussian process regression
Matthias Seeger, Christopher Williams, and Neil Lawrence · 2003
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Nonstationary covariance functions for Gaussian process regression
Christopher J Paciorek and Mark J Schervish · 2004
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A unifying view of sparse approximate Gaussian process regression
Joaquin Quiñonero-Candela and Carl Edward Rasmussen · 2005
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Sparse Gaussian processes using pseudo-inputs
Edward Snelson and Zoubin Ghahramani · 2006
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Gaussian processes for machine learning
Christopher KI Williams and Carl Edward Rasmussen · 2006
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Composition attacks and auxiliary information in data privacy
Srivatsava Ranjit Ganta, Shiva Prasad Kasiviswanathan, and Adam Smith · 2008
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A practical differentially private random decision tree classifier
Geetha Jagannathan, Krishnan Pillaipakkamnatt, and Rebecca N Wright · 2009
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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 · 2009
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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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Functional mechanism: regression analysis under differential privacy
Jun Zhang, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, and Marianne Winslett · 2012
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A stability-based validation procedure for differentially private machine learning
Kamalika Chaudhuri and Staal A Vinterbo · 2013
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Differential privacy for functions and functional data
Privacy preserving RBF kernel support vector machine
Haoran Li, Li Xiong, Lucila Ohno-Machado, and Xiaoqian Jiang · 2014
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An effective privacy preserving algorithm for neighborhood-based collaborative filtering
Tianqing Zhu, Yongli Ren, Wanlei Zhou, Jia Rong, and Ping Xiong · 2014
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Differentially private Bayesian optimization
Matt J Kusner, Jacob R Gardner, Roman Garnett, and Kilian Q Weinberger · 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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Scalable Gaussian processes for characterizing multidimensional change surfaces
William Herlands, Andrew Wilson, Hannes Nickisch, Seth Flaxman, Daniel Neill, Wilbert Van Panhuis, and Eric Xing · 2016
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Rob Hall, Alessandro Rinaldo, and Larry Wasserman · 2013
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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 naive Bayes classification
Jaideep Vaidya, Basit Shafiq, Anirban Basu, and Yuan Hong · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Differentially private random decision forests using smooth sensitivity
Sam Fletcher and Md Zahidul Islam · 2017
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Credit Risk Analytics: The R Companion
Harald Scheule, Daniel Rösch, and Bart Baesens · 2017
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Performance analysis of a privacy constrained kNN recommendation using data sketches
Armita Afsharinejad and Neil Hurley · 2018
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Differentially private Gaussian processes
Michael Thomas Smith, Mauricio A Álvarez, Max Zwiessele, and Neil D Lawrence · 2018
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