Distributed Learning

Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication ICML 2018 Training Neural Networks Using Features Replay NeurIPS 2018 Accelerated consensus via Min-Sum Splitting NeurIPS 2017 Accumulated Gradient Normalization ACML 2017 Adaptive Consensus ADMM for Distributed Optimization ICML 2017 A distributed block coordinate descent method for training l1 regularized linear classifiers JMLR 2017 A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization JMLR 2017 Asynchronous Coordinate Descent under More Realistic Assumptions NeurIPS 2017 Asynchronous Parallel Coordinate Minimization for MAP Inference NeurIPS 2017 Asynchronous Stochastic Gradient Descent with Delay Compensation ICML 2017 Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization NeurIPS 2017 Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent NeurIPS 2017 Coded Distributed Computing for Inverse Problems NeurIPS 2017 Collaborative Deep Learning in Fixed Topology Networks NeurIPS 2017 Collaborative PAC Learning NeurIPS 2017 Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis ICML 2017 Communication-efficient Distributed Sparse Linear Discriminant Analysis AISTATS 2017 Communication-Efficient Learning of Deep Networks from Decentralized Data AISTATS 2017 Communication-efficient Sparse Regression JMLR 2017 Computational Limits of A Distributed Algorithm for Smoothing Spline JMLR 2017 Data Driven Resource Allocation for Distributed Learning AISTATS 2017 Decentralized Collaborative Learning of Personalized Models over Networks AISTATS 2017 Distributed Accelerated Proximal Coordinate Gradient Methods IJCAI 2017 Distributed Adaptive Sampling for Kernel Matrix Approximation AISTATS 2017 Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server JMLR 2017