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 301–310 of 536 papers

TitleStatusHype
Distributed System Identification for Linear Stochastic Systems with Binary Sensors—0
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time—0
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression—0
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition—0
Robust Distributed Optimization With Randomly Corrupted Gradients—0
Accelerating variational quantum algorithms with multiple quantum processors—0
Asynchronous Stochastic Optimization Robust to Arbitrary Delays—0
Learning Autonomy in Management of Wireless Random Networks—0
Decentralized Personalized Federated Learning for Min-Max Problems—0
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques—0
Show:102550
← PrevPage 31 of 54Next →

No leaderboard results yet.