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In distributed learning, the goal is to perform a learning task over data distributed across multiple nodes with minimal (expensive) communication.
Approximations and optimal geometric divide-and-conquer
Matousek, Jiri · 1991
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An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
Bauer, Eric and Kohavi, Ron · 1999
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The Discrepancy Method
Chazelle, Bernard · 2000
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Discriminative training methods for hidden markov models: theory and experiments with perceptron algorithms
Collins, Michael · 2002
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Data streams: algorithms and applications
Muthukrishnan, S · 2005
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The interplay of optimization and machine learning research
Bennett, Kristin P. and Parrado-Hernández, Emilio · 2006
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Multi-pass geometric algorithms
Chan, Timothy M. and Chen, Eric Y · 2007
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Map-reduce for machine learning on multicore
Chu, Cheng Tao, Kim, Sang Kyun, Lin, Yi An, Yu, YuanYuan, Bradski, Gary, Ng, Andrew Y., and Olukotun, Kunle · 2007
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Algorithms for distributed functional monitoring
Cormode, Graham, Muthukrishnan, S., and Yi, Ke · 2008
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Rank minimization via online learning
Meka, Raghu, Jain, Prateek, Caramanis, Constantine, and Dhillon, Inderjit S · 2008
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Neural Network Learning: Theoretical Foundations
Anthony, Martin and Bartlett, Peter L · 2009
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Efficient large-scale distributed training of conditional maximum entropy models
Mann, Gideon, McDonald, Ryan, Mohri, Mehryar, Silberman, Nathan, and Walker, Dan · 2009
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Optimal sampling from distributed streams
Cormode, Graham, Muthukrishnan, S., Yi, Ke, and Zhang, Qin · 2010
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Optimal distributed online prediction using mini-batches
Dekel, Ofer, Gilad-Bachrach, Ran, Shamir, Ohad, and Xiao, Lin · 2010
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Distributed dual averaging in networks
Duchi, John, Agarwal, Alekh, and Wainwright, Martin · 2010
Bundle methods for regularized risk minimization
Teo, Choon Hui, Vishwanthan, S.V.N., Smola, Alex J., and Le, Quoc V · 2010
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Parallelized stochastic gradient descent
Zinkevich, Martin, Weimer, Markus, Smola, Alex, and Li, Lihong · 2010
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Distributed delayed stochastic optimization
Agarwal, Alekh and Duchi, John · 2011
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Scaling up machine learning: Parallel and distributed approaches, 2011
Bekkerman, Ron, Bilenko, Mikhail, and Langford, John · 2011
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LIBSVM: A library for support vector machines
Chang, Chih Chung and Lin, Chih Jen · 2011
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Algorithms and hardness results for parallel large margin learning
Servedio, Rocco A. and Long, Phil · 2011
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UCI machine learning repository, 2010
Frank, A. and Asuncion, A · 2010
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Distributed training strategies for the structured perceptron
McDonald, Ryan, Hall, Keith, and Mann, Gideon · 2010
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The multiplicative weights update method: a meta algorithm and applications
Arora, Sanjeev, Hazan, Elad, and Kale, Satyen
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Fast algorithms for approximate semidefinite programming using the multiplicative weights update method
Arora, Sanjeev, Hazan, Elad, and Kale, Satyen
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Distributed learning, communication complexity and privacy
Balcan, Maria Florina, Blum, Avrim, Fine, Shai, and Mansour, Yishay · 2012
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Protocols for learning classifiers on distributed data
Daumé III, Hal, Phillips, Jeff, Saha, Avishek, and Venkatasubramanian, Suresh · 2012
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