Distributed Learning

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