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Found 11 papers, 3 papers with code
Twitter Homophily: Network Based Prediction of User's Occupation
In this paper, we investigate the importance of social network information compared to content information in the prediction of a Twitter user{'}s occupational class.
Hemingway: Modeling Distributed Optimization Algorithms
Distributed optimization algorithms are widely used in many industrial machine learning applications.
Revisiting Distributed Synchronous SGD
Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony.
CYCLADES: Conflict-free Asynchronous Machine Learning
We present CYCLADES, a general framework for parallelizing stochastic optimization algorithms in a shared memory setting.
Revisiting Distributed Synchronous SGD
Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony.
Perturbed Iterate Analysis for Asynchronous Stochastic Optimization
We demonstrate experimentally on a 16-core machine that the sparse and parallel version of SVRG is in some cases more than four orders of magnitude faster than the standard SVRG algorithm.
Parallel Correlation Clustering on Big Graphs
We present C4 and ClusterWild!, two algorithms for parallel correlation clustering that run in a polylogarithmic number of rounds and achieve nearly linear speedups, provably.
Parallel Double Greedy Submodular Maximization
Many machine learning problems can be reduced to the maximization of submodular functions.
MLI: An API for Distributed Machine Learning
MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing.
Optimistic Concurrency Control for Distributed Unsupervised Learning
Research on distributed machine learning algorithms has focused primarily on one of two extremes - algorithms that obey strict concurrency constraints or algorithms that obey few or no such constraints.
Conditions for Convergence in Regularized Machine Learning Objectives
Analysis of the convergence rates of modern convex optimization algorithms can be achived through binary means: analysis of emperical convergence, or analysis of theoretical convergence.
