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- W3013305860 abstract "In this paper, we present stochastic optimization for empirical risk minimization over directed graphs. Using a novel information fusion approach that utilizes both row- and column-stochastic weights simultaneously, we propose $mathcal{S}mathcal{A}mathcal{B}$, a decentralized stochastic gradient method with gradient tracking, and show that the proposed algorithm converges linearly to an error ball around the optimal solution with a constant step-size. We provide a sketch of the convergence analysis as well as the generalization of the proposed algorithm. Finally, we illustrate the theoretical results with the help of experiments with real data." @default.
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- W3013305860 date "2019-11-01" @default.
- W3013305860 modified "2023-10-18" @default.
- W3013305860 title "Distributed empirical risk minimization over directed graphs" @default.
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- W3013305860 doi "https://doi.org/10.1109/ieeeconf44664.2019.9049065" @default.
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