SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 421–430 of 536 papers

TitleStatusHype
PowerSGD: Practical Low-Rank Gradient Compression for Distributed OptimizationCode0
Accelerated Sparsified SGD with Error Feedback—0
Distributed estimation of the inverse Hessian by determinantal averaging—0
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension—0
OverSketched Newton: Fast Convex Optimization for Serverless SystemsCode0
Byzantine Fault Tolerant Distributed Linear Regression—0
Differentially Private Consensus-Based Distributed Optimization—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems—0
On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication—0
Show:102550
← PrevPage 43 of 54Next →

No leaderboard results yet.