Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: AIDE: Fast and Communication Efficient Distributed Optimization
Communication Complexity of Distributed Convex Learning and Optimization
Scaling Up Distributed Stochastic Gradient Descent Using Variance Reduction.
Stochastic, Distributed and Federated Optimization for Machine Learning
Decentralized Multi-Agent Optimization via Dual Decomposition
A Little Is Enough: Circumventing Defenses For Distributed Learning
Staleness-aware Async-SGD for Distributed Deep Learning
Speeding up distributed machine learning using codes
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication
Improving Distributed Gradient Descent Using Reed-Solomon Codes
Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting
Asynchronous Decentralized Parallel Stochastic Gradient Descent
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling
Federated Optimization for Heterogeneous Networks
Distributed stochastic optimization for deep learning (thesis)
PrIter: A Distributed Framework for Prioritizing Iterative Computations
Variance Reduction for Distributed Stochastic Gradient Descent
Multi-Block ADMM for Big Data Optimization in Modern Communication Networks
Optimized Distributed Work-Stealing
Fast-and-Light Stochastic ADMM
Data Mining: Document Classification using Naive Bayes Classifier
Cost Estimation Using Extended Use Case Point (e-UCP) Model
Organizational Culture and Effectiveness : Can American Theory Be Applied in Russia ?
Sparse Online Learning via Truncated Gradient
Dexterous manipulation with underactuated elastic hands