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Distributed Learning

1100 directly classified papers

Papers per year

Papers

Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models NIPS 2014
Scalable and Robust Bayesian Inference via the Median Posterior ICML 2014
Distributed Parameter Estimation in Probabilistic Graphical Models NIPS 2014
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning NIPS 2014
Scalable Variational Bayesian Matrix Factorization with Side Information AISTATS 2014
Distributed Estimation, Information Loss and Exponential Families NIPS 2014
Distributed Stochastic Gradient MCMC ICML 2014
Improved Distributed Principal Component Analysis NIPS 2014
Distributed Balanced Clustering via Mapping Coresets NIPS 2014
Large-scale L-BFGS using MapReduce NIPS 2014
Communication-Efficient Distributed Optimization using an Approximate Newton-type Method ICML 2014
Communication-Efficient Distributed Dual Coordinate Ascent NIPS 2014
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis NIPS 2014
Distributed Bayesian Posterior Sampling via Moment Sharing NIPS 2014
On Communication Cost of Distributed Statistical Estimation and Dimensionality NIPS 2014
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning NIPS 2014
Communication Efficient Distributed Machine Learning with the Parameter Server NIPS 2014
Efficient Distributed Topic Modeling with Provable Guarantees AISTATS 2014
Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data AISTATS 2014
Median Selection Subset Aggregation for Parallel Inference NIPS 2014
Principal Component Analysis and Higher Correlations for Distributed Data COLT 2014
Decomposing Parameter Estimation Problems NIPS 2014
Asynchronous Distributed ADMM for Consensus Optimization ICML 2014
A Reliable Effective Terascale Linear Learning System JMLR 2014
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data NIPS 2013