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- W2591423585 abstract "The asymptotic behavior of the stochastic gradient algorithm using biased gradient estimates is analyzed. Relying on arguments based on dynamic system theory (chain-recurrence) and differential geometry (Yomdin theorem and Lojasiewicz inequalities), upper bounds on the asymptotic bias of this algorithm are derived. The results hold under mild conditions and cover a broad class of algorithms used in machine learning, signal processing and statistics." @default.
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- W2591423585 date "2017-12-01" @default.
- W2591423585 modified "2023-09-23" @default.
- W2591423585 title "Asymptotic bias of stochastic gradient search" @default.
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- W2591423585 doi "https://doi.org/10.1214/16-aap1272" @default.
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