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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 61–70 of 536 papers

TitleStatusHype
Differentially Private Distributed Estimation and LearningCode0
OverSketched Newton: Fast Convex Optimization for Serverless SystemsCode0
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleCode0
DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client SynchronizationCode0
Cooperative Tuning of Multi-Agent Optimal Control SystemsCode0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Communication Efficient Distributed Optimization using an Approximate Newton-type MethodCode0
Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh NetworksCode0
CoCoA: A General Framework for Communication-Efficient Distributed OptimizationCode0
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved RatesCode0
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