On the stability of the stochastic gradient Langevin algorithm with dependent data stream

Miklós Rásonyi and Kinga Tikosi

Statistics & Probability Letters, 2022, vol. 182, issue C

Abstract: We prove, under mild conditions, that the stochastic gradient Langevin dynamics converges to a limiting law as time tends to infinity, even in the case where the driving data sequence is dependent.

Keywords: Stochastic gradient; Langevin dynamics; Dependent data (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.spl.2021.109321

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