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Distributed Learning

123 directly classified papers

Papers per year

Papers

Decentralized Cooperative Stochastic Bandits NIPS 2019
Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning NIPS 2019
Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up NIPS 2019
Rates of Convergence for Large-scale Nearest Neighbor Classification NIPS 2019
Double Quantization for Communication-Efficient Distributed Optimization NIPS 2019
The Convergence of Sparsified Gradient Methods NIPS 2018
cpSGD: Communication-efficient and differentially-private distributed SGD NIPS 2018
Gradient Sparsification for Communication-Efficient Distributed Optimization NIPS 2018
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks NIPS 2018
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights NIPS 2018
Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training NIPS 2018
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding NIPS 2017
Asynchronous Coordinate Descent under More Realistic Assumptions NIPS 2017
Collaborative PAC Learning NIPS 2017
A distributed block coordinate descent method for training l1 regularized linear classifiers JMLR 2017
Communication-efficient Sparse Regression JMLR 2017
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent NIPS 2017
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning NIPS 2017
Stochastic Gradient MCMC with Stale Gradients NIPS 2016
A Consensus-Based Framework for Distributed Bundle Adjustment CVPR 2016
Bootstrap Model Aggregation for Distributed Statistical Learning NIPS 2016
Cyclades: Conflict-free Asynchronous Machine Learning NIPS 2016
Communication Complexity of Distributed Convex Learning and Optimization NIPS 2015
Secure Multi-party Differential Privacy NIPS 2015
Variational Consensus Monte Carlo NIPS 2015