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Distributed Learning

1100 directly classified papers

Papers per year

Papers

Protocols for Learning Classifiers on Distributed Data AISTATS 2012
Distributed Probabilistic Learning for Camera Networks with Missing Data NIPS 2012
Distributed Learning, Communication Complexity and Privacy COLT 2012
Divide-and-Conquer Matrix Factorization NIPS 2011
Parallel Gibbs Sampling: From Colored Fields to Thin Junction Trees AISTATS 2011
Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent NIPS 2011
Distributed Delayed Stochastic Optimization NIPS 2011
Preliminary Results in Decentralized Estimation for Single-Beacon Acoustic Underwater Navigation RSS 2010
A Streaming Parallel Decision Tree Algorithm JMLR 2010
Distributed Dual Averaging In Networks NIPS 2010
Consensus-Based Distributed Support Vector Machines JMLR 2010
Distributed Optimization with Pairwise Constraints and its Application to Multi-robot Path Planning RSS 2010
Parallelized Stochastic Gradient Descent NIPS 2010
Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units NIPS 2009
Distributed Algorithms for Topic Models JMLR 2009
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process NIPS 2009
Slow Learners are Fast NIPS 2009
Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models NIPS 2009
A Scalable Hierarchical Distributed Language Model NIPS 2008
Asynchronous Distributed Learning of Topic Models NIPS 2008
A Massively Parallel Digital Learning Processor NIPS 2008
Distributed Inference for Latent Dirichlet Allocation NIPS 2007
Parallelizing Support Vector Machines on Distributed Computers NIPS 2007
Privacy-Preserving Belief Propagation and Sampling NIPS 2007
Map-Reduce for Machine Learning on Multicore NIPS 2006