Distributed Learning

GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training NeurIPS 2018 HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty RSS 2018 IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures ICML 2018 Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms ICML 2018 LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning NeurIPS 2018 Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under ℓ_p Distances ICML 2018 Mesh-TensorFlow: Deep Learning for Supercomputers NeurIPS 2018 Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems AISTATS 2018 Optimal Algorithms for Non-Smooth Distributed Optimization in Networks NeurIPS 2018 Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods ICML 2018 Parallel Bayesian Network Structure Learning ICML 2018 Parallelizing Spectrally Regularized Kernel Algorithms JMLR 2018 Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training NeurIPS 2018 Pocket: Elastic Ephemeral Storage for Serverless Analytics OSDI 2018 Proximal SCOPE for Distributed Sparse Learning NeurIPS 2018 Ray: A Distributed Framework for Emerging AI Applications OSDI 2018 RLlib: Abstractions for Distributed Reinforcement Learning ICML 2018 signSGD: Compressed Optimisation for Non-Convex Problems ICML 2018 Simple, Distributed, and Accelerated Probabilistic Programming NeurIPS 2018 Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD AISTATS 2018 Snap ML: A Hierarchical Framework for Machine Learning NeurIPS 2018 Sparsified SGD with Memory NeurIPS 2018 The Convergence of Sparsified Gradient Methods NeurIPS 2018 The Hidden Vulnerability of Distributed Learning in Byzantium ICML 2018 Tight Bounds for Collaborative PAC Learning via Multiplicative Weights NeurIPS 2018