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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
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleCode0
Differentially Private Distributed Estimation and LearningCode0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Cooperative Tuning of Multi-Agent Optimal Control SystemsCode0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
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
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural NetworksCode0
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