Research Explorer
Papers
Trends
Conferences
Explore
Authors
Topics
Keywords
Papers
Trends
Conferences
Explore
Authors
Topics
Keywords
Achievements
About
Methodology
← Optimization & Theory
Machine Learning
›
Optimization & Theory
›
Distributed Learning
1100 directly classified papers
Papers per year
2006: 1
2007: 3
2008: 3
2009: 5
2010: 6
2011: 4
2012: 9
2013: 20
2014: 27
2015: 18
2016: 44
2017: 49
2018: 70
2019: 92
2020: 108
2021: 125
2022: 127
2023: 145
2024: 125
2025: 89
2026: 30
Papers
Hydra: a federated resource manager for data-center scale analytics
NSDI 2019
Scaling Community Cellular Networks with CommunityCellularManager
NSDI 2019
Optimization of Inf-Convolution Regularized Nonconvex Composite Problems
AISTATS 2019
Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks
AISTATS 2019
Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models
AISTATS 2019
Learning to Learn Gradient Aggregation by Gradient Descent
IJCAI 2019
Combining ADMM and the Augmented Lagrangian Method for Efficiently Handling Many Constraints
IJCAI 2019
Fully Distributed Bayesian Optimization with Stochastic Policies
IJCAI 2019
Building Personalized Simulator for Interactive Search
IJCAI 2019
SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition
JMLR 2019
spark-crowd: A Spark Package for Learning from Crowdsourced Big Data
JMLR 2019
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning
JMLR 2019
An asymptotic analysis of distributed nonparametric methods
JMLR 2019
Distributed Inference for Linear Support Vector Machine
JMLR 2019
Optimal Convergence Rates for Convex Distributed Optimization in Networks
JMLR 2019
Distributed Learning over Unreliable Networks
ICML 2019
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
ICML 2019
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning
AAAI 2019
Communication-Efficient Stochastic Gradient MCMC for Neural Networks
AAAI 2019
Stochastic Gradient Push for Distributed Deep Learning
ICML 2019
Bayesian Nonparametric Federated Learning of Neural Networks
ICML 2019
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization
ICML 2019
On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization
ICML 2019
Scalable Distributed DL Training: Batching Communication and Computation
AAAI 2019
Communication-Optimal Distributed Dynamic Graph Clustering
AAAI 2019
<
1
…
30
31
32
…
44
>