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Distributed Learning

1100 directly classified papers

Papers per year

Papers

Optimal Algorithms for Non-Smooth Distributed Optimization in Networks NIPS 2018
Distributed Stochastic Optimization via Adaptive SGD NIPS 2018
COLA: Decentralized Linear Learning NIPS 2018
Geometric Lower Bounds for Distributed Parameter Estimation under Communication Constraints COLT 2018
Ray: A Distributed Framework for Emerging AI Applications OSDI 2018
Distributed $k$-Clustering for Data with Heavy Noise NIPS 2018
Pocket: Elastic Ephemeral Storage for Serverless Analytics OSDI 2018
Deconstructing RDMA-enabled Distributed Transactions: Hybrid is Better! OSDI 2018
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training NIPS 2018
Communication Efficient Parallel Algorithms for Optimization on Manifolds NIPS 2018
BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training NIPS 2018
Sparsified SGD with Memory NIPS 2018
Gradient Sparsification for Communication-Efficient Distributed Optimization NIPS 2018
Communication Compression for Decentralized Training NIPS 2018
Parallelizing Spectrally Regularized Kernel Algorithms JMLR 2018
CoCoA: A General Framework for Communication-Efficient Distributed Optimization JMLR 2018
Slow and Stale Gradients Can Win the Race: Error-Runtime Trade-offs in Distributed SGD AISTATS 2018
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization NIPS 2018
Byzantine Stochastic Gradient Descent NIPS 2018
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication NIPS 2018
The Convergence of Sparsified Gradient Methods NIPS 2018
ATOMO: Communication-efficient Learning via Atomic Sparsification NIPS 2018
Distributed Pareto Optimization for Subset Selection IJCAI 2018
Distributed Primal-Dual Optimization for Non-uniformly Distributed Data IJCAI 2018
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates ICML 2018