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 81–90 of 536 papers

TitleStatusHype
Local Methods with Adaptivity via Scaling—0
Log-Scale Quantization in Distributed First-Order Methods: Gradient-based Learning from Distributed Data—0
Differentially-Private Distributed Model Predictive Control of Linear Discrete-Time Systems with Global Constraints—0
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable ConvergenceCode1
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication—0
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance—0
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization—0
Distributed Traffic Signal Control via Coordinated Maximum Pressure-plus-Penalty—0
Estimation Network Design framework for efficient distributed optimization—0
Rate Analysis of Coupled Distributed Stochastic Approximation for Misspecified Optimization—0
Show:102550
← PrevPage 9 of 54Next →

No leaderboard results yet.