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Asynchronous Stochastic Composition Optimization with Variance Reduction
[article]
2018
arXiv
pre-print
Composition optimization has drawn a lot of attention in a wide variety of machine learning domains from risk management to reinforcement learning. Existing methods solving the composition optimization problem often work in a sequential and single-machine manner, which limits their applications in large-scale problems. To address this issue, this paper proposes two asynchronous parallel variance reduced stochastic compositional gradient (AsyVRSC) algorithms that are suitable to handle
arXiv:1811.06396v1
fatcat:gophcoez6befbccofx6s55nere