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Distributed Learning

1100 directly classified papers

Papers per year

Papers

Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms ICML 2016
Cyclades: Conflict-free Asynchronous Machine Learning NIPS 2016
Online and Distributed Bayesian Moment Matching for Parameter Learning in Sum-Product Networks AISTATS 2016
Gemini: A Computation-Centric Distributed Graph Processing System OSDI 2016
Fast and Concurrent RDF Queries with RDMA-Based Distributed Graph Exploration OSDI 2016
Distributed Flexible Nonlinear Tensor Factorization NIPS 2016
A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order NIPS 2016
Distributed Clustering of Linear Bandits in Peer to Peer Networks ICML 2016
A Multi-Batch L-BFGS Method for Machine Learning NIPS 2016
Supervised Neighborhoods for Distributed Nonparametric Regression AISTATS 2016
A Communication-Efficient Parallel Algorithm for Decision Tree NIPS 2016
Scalable Gaussian Process Classification via Expectation Propagation AISTATS 2016
Blind Recovery of Perceptual Models in Distributed Speech and Audio Coding INTERSPEECH 2016
Virtual Machines and Containers as a Platform for Experimentation INTERSPEECH 2016
A primal-dual method for conic constrained distributed optimization problems NIPS 2016
Stochastic Gradient MCMC with Stale Gradients NIPS 2016
JetStream: Cluster-Scale Parallelization of Information Flow Queries OSDI 2016
Network Requirements for Resource Disaggregation OSDI 2016
Distributed Submodular Maximization JMLR 2016
DSA: Decentralized Double Stochastic Averaging Gradient Algorithm JMLR 2016
DECOrrelated feature space partitioning for distributed sparse regression NIPS 2016
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale NIPS 2016
Communication-Optimal Distributed Clustering NIPS 2016
Exponential Stochastic Cellular Automata for Massively Parallel Inference AISTATS 2016
Horizontally Scalable Submodular Maximization ICML 2016