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Consider two data providers, each maintaining records of different feature sets about common entities.
The Hungarian method for the assignment problem
H.-W. Kuhn · 1955
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A survey of heuristics for the weighted matching problem
D. Avis · 1983
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Matrix Analysis
R. Bhatia · 1997
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UCI repository of machine learning databases, 1998
C. L. Blake, E. Keogh, and C.J. Merz · 1998
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Integration of heterogeneous databases without common domains using queries based on textual similarity
W.-W. Cohen · 1998
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Real-world data is dirty: Data cleansing and the merge/purge problem
M.-A. Hernández and S.-J. Stolfo · 1998
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Improved boosting algorithms using confidence-rated predictions
R. E. Schapire and Y. Singer · 1999
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Rademacher and gaussian complexities: Risk bounds and structural results
P.-L. Bartlett and S. Mendelson · 2002
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Introduction to the Mathematical and Statistical Foundations of Econometrics
H.-J. Bierens · 2004
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Convexity, classification, and risk bounds
P. Bartlett, M. Jordan, and J. D. McAuliffe · 2006
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Record linkage: similarity measures and algorithms
N. Koudas, S. Sarawagi, and D. Srivastava · 2006
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On the complexity of linear prediction: Risk bounds, margin bounds, and regularization
S. Kakade, K. Sridharan, and A. Tewari · 2008
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Accurate synthetic generation of realistic personal information
P. Christen and A. Pudjijono · 2009
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Bregman divergences and surrogates for learning
R. Nock and F. Nielsen · 2009
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Record linkage
W.-E. Winkler · 2009
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Composite binary losses
M.-D. Reid and R.-C. Williamson · 2010
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Data matching: concepts and techniques for record linkage, entity resolution, and duplicate detection
P. Christen · 2012
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Scalable and secure logistic regression via homomorphic encryption
Y. Aono, T. Hayashi, L. Trieu Phong, and L. Wang · 2016
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Federated learning: Strategies for improving communication efficiency
J. Konec̆ný, H.-B. McMahan, F.-X. Yu, P. Richtarik, A.-T. Suresh, and D. Bacon · 2016
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Fast learning from distributed datasets without entity matching
G. Patrini, R. Nock, S. Hardy, and T. Caetano · 2016
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Privlogit: Efficient privacy-preserving logistic regression by tailoring numerical optimizers
Wei Xie, Yang Wang, Steven M. Boker, and Donald E. Brown · 2016
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The computational complexity of weighted greedy matching
A. Deligkas, G.-B. Mertzios, and P.-G. Spirakis · 2017
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Privacy-preserving entity resolution and logistic regression on encrypted data
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Privacy-preserving ridge regression on hundreds of millions of records
V. Nikolaenko, U. Weinsberg, S. Ioannidis, M. Joye, D. Boneh, and N. Taft · 2013
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Efficient private record linkage of very large datasets
R. Schnell · 2013
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(Almost) no label no cry
G. Patrini, R. Nock, P. Rivera, and T. Caetano · 2014
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Learning with symmetric label noise: The importance of being unhinged
B. van Rooyen, A. Menon, and R.-C. Williamson · 2015
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M. Djatmiko, S. Hardy, W. Henecka, H. Ivey-Law, M. Ott, G. Patrini, G. Smith, B. Thorne, and D. Wu · 2017
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Encrypted accelerated least squares regression
P.-M. Esperança, L.-J.-M. Aslett, and C.-C. Holmes · 2017
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Privacy-preserving distributed linear regression on high-dimensional data
A. Gascón, P. Schoppmann, B. Balle, M. Raykova, J. Doerner, S. Zahur, and D. Evans · 2017
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Privacy-preserving ridge regression on distributed data
I. Giacomelli, S. Jha, C.-D. Page, and K. Yoon · 2017
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S. Hardy, W. Henecka, H. Ivey-Law, R. Nock, G. Patrini, G. Smith, and B. Thorne · 2017
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The rise of the data marketplace
D. Wells · 2017
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