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We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are distributed (unevenly) over an extremely large number of \nodes, but the goal remains to train a high-quality centralized model.
Distributed learning, communication complexity and privacy
Maria-Florina Balcan, Avrim Blum, Shai Fine, and Yishay Mansour · 2012
Earlier work this paper cites.
Communication-efficient algorithms for statistical optimization
Yuchen Zhang, Martin J Wainwright, and John C Duchi · 2012
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Accelerating stochastic gradient descent using predictive variance reduction
Rie Johnson and Tong Zhang · 2013
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Semi-stochastic gradient descent methods
Jakub Konečný and Peter Richtárik · 2013
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Distributed coordinate descent method for learning with big data
Peter Richtárik and Martin Takáč · 2013
Earlier work this paper cites.
Communication efficient distributed optimization using an approximate newton-type method
Ohad Shamir, Nathan Srebro, and Tong Zhang · 2013
Cited alongside, same era.
Trading computation for communication: Distributed stochastic dual coordinate ascent
Tianbao Yang · 2013
Cited alongside, same era.
Information-theoretic lower bounds for distributed statistical estimation with communication constraints
Yuchen Zhang, John Duchi, Michael I Jordan, and Martin J Wainwright · 2013
Cited alongside, same era.
Fast distributed coordinate descent for non-strongly convex losses
Olivier Fercoq, Zheng Qu, Peter Richtárik, and Martin Takác · 2014
Cited alongside, same era.
Communication-efficient distributed dual coordinate ascent
Martin Jaggi, Virginia Smith, Martin Takác, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, and Michael I Jordan · 2014
Cited alongside, same era.
Distributed stochastic optimization and learning
Ohad Shamir and Nathan Srebro · 2014
Later among the works it cites.
Communication complexity of distributed convex learning and optimization
Yossi Arjevani and Ohad Shamir · 2015
Closest in time.
Adding vs. averaging in distributed primal-dual optimization
Chenxin Ma, Virginia Smith, Martin Jaggi, Michael I Jordan, Peter Richtárik, and Martin Takáč · 2015
Closest in time.
Communication-efficient distributed optimization of self-concordant empirical loss
Yuchen Zhang and Lin Xiao · 2015
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