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Distributed Learning

1100 directly classified papers

Papers per year

Papers

Communication-Efficient Agnostic Federated Averaging INTERSPEECH 2021
Proof of Learning (PoLe): Empowering Machine Learning with Consensus Building on Blockchains (Demo) AAAI 2021
Distributed Machine Learning with Sparse Heterogeneous Data NIPS 2021
Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition NIPS 2021
EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition AAAI 2021
Spectral vertex sparsifiers and pair-wise spanners over distributed graphs ICML 2021
STL-SGD: Speeding Up Local SGD with Stagewise Communication Period AAAI 2021
BASGD: Buffered Asynchronous SGD for Byzantine Learning ICML 2021
Accumulated Decoupled Learning with Gradient Staleness Mitigation for Convolutional Neural Networks ICML 2021
Decentralized Riemannian Gradient Descent on the Stiefel Manifold ICML 2021
Elastic Resource Sharing for Distributed Deep Learning NSDI 2021
A Flexible Framework for Communication-Efficient Machine Learning AAAI 2021
NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization JMLR 2021
COKE: Communication-Censored Decentralized Kernel Learning JMLR 2021
Exponential Graph is Provably Efficient for Decentralized Deep Training NIPS 2021
ATP: In-network Aggregation for Multi-tenant Learning NSDI 2021
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates ICML 2020
Statistical Guarantees of Distributed Nearest Neighbor Classification NIPS 2020
Practical Low-Rank Communication Compression in Decentralized Deep Learning NIPS 2020
Indirect Stochastic Gradient Quantization and Its Application in Distributed Deep Learning AAAI 2020
Fine-Grained Replicated State Machines for a Cluster Storage System NSDI 2020
Gryff: Unifying Consensus and Shared Registers NSDI 2020
Millions of Tiny Databases NSDI 2020
Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning AAAI 2020
AutoDAL: Distributed Active Learning with Automatic Hyperparameter Selection AAAI 2020