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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 526–536 of 536 papers

TitleStatusHype
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and OptimizationCode0
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth RegularizationCode0
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
Error Feedback Shines when Features are RareCode0
Byzantine-Robust Loopless Stochastic Variance-Reduced GradientCode0
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex OptimizationCode0
Optimization for Large-Scale Machine Learning with Distributed Features and ObservationsCode0
SlowMo: Improving Communication-Efficient Distributed SGD with Slow MomentumCode0
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk MinimizationCode0
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation RetrievalCode0
OverSketched Newton: Fast Convex Optimization for Serverless SystemsCode0
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