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Distributed Optimization

The goal of Distributed Optimization is to optimize a certain objective defined over millions of billions of data that is distributed over many machines by utilizing the computational power of these machines.

Source: Analysis of Distributed StochasticDual Coordinate Ascent

Papers

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Showing 511–520 of 536 papers

TitleStatusHype
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved RatesCode0
Manifold Identification for Ultimately Communication-Efficient Distributed OptimizationCode0
Robust Learning from Untrusted SourcesCode0
Opportunistic Routing in Wireless Communications via Learnable State-Augmented PoliciesCode0
Distributed Optimization, Averaging via ADMM, and Network TopologyCode0
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine LearningCode0
Communication Efficient Distributed Optimization using an Approximate Newton-type MethodCode0
Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh NetworksCode0
Optimal algorithms for smooth and strongly convex distributed optimization in networksCode0
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual FrameworkCode0
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