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← Optimization & Theory
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Optimization & Theory
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Distributed Learning
1100 directly classified papers
Papers per year
2006: 1
2007: 3
2008: 3
2009: 5
2010: 6
2011: 4
2012: 9
2013: 20
2014: 27
2015: 18
2016: 44
2017: 49
2018: 70
2019: 92
2020: 108
2021: 125
2022: 127
2023: 145
2024: 125
2025: 89
2026: 30
Papers
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
ICML 2018
Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters
JMLR 2018
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
NIPS 2018
RLlib: Abstractions for Distributed Reinforcement Learning
ICML 2018
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
NIPS 2018
Snap ML: A Hierarchical Framework for Machine Learning
NIPS 2018
Distributed Clustering via LSH Based Data Partitioning
ICML 2018
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
ICML 2018
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under Uncertainty
RSS 2018
Simple, Distributed, and Accelerated Probabilistic Programming
NIPS 2018
Bayesian Distributed Stochastic Gradient Descent
NIPS 2018
Accelerated consensus via Min-Sum Splitting
NIPS 2017
Parallel Streaming Wasserstein Barycenters
NIPS 2017
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
NIPS 2017
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization
NIPS 2017
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
NIPS 2017
Empirical Evaluation of Parallel Training Algorithms on Acoustic Modeling
INTERSPEECH 2017
Fast Parallel Training of Neural Language Models
IJCAI 2017
Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data
IJCAI 2017
On the Computational Complexity of Gossip Protocols
IJCAI 2017
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
NIPS 2017
Collaborative Deep Learning in Fixed Topology Networks
NIPS 2017
Asynchronous Stochastic Gradient Descent with Delay Compensation
ICML 2017
Adaptive Consensus ADMM for Distributed Optimization
ICML 2017
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement
JMLR 2017
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