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A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients.
Sex bias in graduate admissions: Data from Berkeley
P. J. Bickel, E. A. Hammel, and J. W. O’Connell · 1975
Earlier work this paper cites.
Problem complexity and Method Efficiency in Optimization
Arkadii Semenovich Nemirovski and David Berkovich Yudin · 1983
Earlier work this paper cites.
Adaptive language modeling using minimum discriminant estimation
S. Della Pietra, V. Della Pietra, R. L. Mercer, and S. Roukos · 1992
Earlier work this paper cites.
Building a large annotated corpus of english: The penn treebank
Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini · 1993
Earlier work this paper cites.
Maximum a posteriori estimation for multivariate gaussian mixture observations of Markov chains
Jean-Luc Gauvain and Chin-Hui · 1994
Earlier work this paper cites.
Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models
C. J. Legetter and Phil C. Woodland · 1995
Earlier work this paper cites.
A Maximum Entropy Approach to Adaptive Statistical Language Modeling
Roni Rosenfeld · 1996
Earlier work this paper cites.
UCI repository of machine learning databases, Irvine, University of California
Catherine L Blake · 1998
Earlier work this paper cites.
Statistical Methods for Speech Recognition
Frederick Jelinek · 1998
Earlier work this paper cites.
Empirical margin distributions and bounding the generalization error of combined classifiers
Vladmir Koltchinskii and Dmitry Panchenko · 2002
Earlier work this paper cites.
Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class
Aleix M. Martínez · 2002
Earlier work this paper cites.
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Brian Roark and Michiel Bacchiani · 2003
Earlier work this paper cites.
Clustering with Bregman divergences
Arindam Banerjee, Srujana Merugu, Inderjit S Dhillon, and Joydeep Ghosh · 2005
Earlier work this paper cites.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
Earlier work this paper cites.
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
John Blitzer, Mark Dredze, and Fernando Pereira · 2007
Earlier work this paper cites.
Frustratingly Hard Domain Adaptation for Parsing
Mark Dredze, John Blitzer, Pratha Pratim Talukdar, Kuzman Ganchev, Joao Graca, and Fernando Pereira · 2007
Earlier work this paper cites.
The minimum description length principle
Peter D. Grünwald · 2007
Earlier work this paper cites.
Instance Weighting for Domain Adaptation in NLP
Jing Jiang and ChengXiang Zhai · 2007
Earlier work this paper cites.
Cross-domain video concept detection using adaptive svms
Jun Yang, Rong Yan, and Alexander G. Hauptmann · 2007
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A survey on transfer learning
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Learning transferable features with deep adaptation networks
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Simultaneous deep transfer across domains and tasks
Eric Tzeng, Judy Hoffman, Trevor Darrell, and Kate Saenko · 2015
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Multi-source domain adaptation: A causal view
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A minimax approach to supervised learning
Farzan Farnia and David Tse · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Later among the works it cites.
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, and Haoyang Zeng · 2017
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Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Distributed mean estimation with limited communication
Ananda Theertha Suresh, Felix X Yu, Sanjiv Kumar, and H Brendan McMahan · 2017
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
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Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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