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In decentralized networks (of sensors, connected objects, etc.), there is an important need for efficient algorithms to optimize a global cost function, for instance to learn a global model from the local data collected by each computing unit.
Algebra connectivity of graphs
Fiedler, Miroslav · 1973
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Problems in decentralized decision making and computation
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Chung, Fan · 1997
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Modern Graph Theory , volume 184
Bollobás, Béla · 1998
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Collective dynamics of ‘small-world’networks
Watts, Duncan J and Strogatz, Steven H · 1998
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Gossip-Based Computation of Aggregate Information
Kempe, David, Dobra, Alin, and Gehrke, Johannes · 2003
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Statistical Inference on Graphs
Biau, Gérard and Bleakley, Kevin · 2006
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Ranking and Empirical Minimization of U-statistics
Clémençon, Stéphan, Lugosi, Gàbor, and Vayatis, Nicolas · 2008
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Regularized Distance Metric Learning: Theory and Algorithm
Jin, R., Wang, S., and Zhou, Y · 2009
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Distributed Subgradient Methods for Multi-Agent Optimization
Nedić, Angelia and Ozdaglar, Asuman E · 2009
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Gossip Algorithms for Computing U-Statistics
Pelckmans, Kristiaan and Suykens, Johan · 2009
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Distances of probability measures and random variables
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A Randomized Incremental Subgradient Method for Distributed Optimization in Networked Systems
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A Binary Classification Framework for Two-Stage Multiple Kernel Learning
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Distributed dual averaging method for multi-agent optimization with quantized communication
Yuan, Deming, Xu, Shengyuan, Zhao, Huanyu, and Rong, Lina · 2012
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Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization
Bianchi, Pascal and Jakubowicz, Jérémie · 2013
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Asynchronous Distributed Optimization using a Randomized Alternating Direction Method of Multipliers
Iutzeler, Franck, Bianchi, Pascal, Ciblat, Philippe, and Hachem, Walid · 2013
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Extending Gossip Algorithms to Distributed Estimation of U-Statistics
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