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

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  • Rásonyi, Miklós
  • Tikosi, Kinga

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.

Suggested Citation

  • Rásonyi, Miklós & Tikosi, Kinga, 2022. "On the stability of the stochastic gradient Langevin algorithm with dependent data stream," Statistics & Probability Letters, Elsevier, vol. 182(C).
    • Handle: RePEc:eee:stapro:v:182:y:2022:i:c:s0167715221002789
    DOI: 10.1016/j.spl.2021.109321
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    References listed on IDEAS

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    1. Lovas, Attila & Rásonyi, Miklós, 2021. "Markov chains in random environment with applications in queuing theory and machine learning," Stochastic Processes and their Applications, Elsevier, vol. 137(C), pages 294-326.
    2. Bal'azs Gerencs'er & Mikl'os R'asonyi, 2020. "Invariant measures for multidimensional fractional stochastic volatility models," Papers 2002.04832, arXiv.org, revised Aug 2021.
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    Cited by:

    1. Ahmed Nafidi & Abdenbi El Azri & Ramón Gutiérrez-Sánchez, 2023. "A Stochastic Schumacher Diffusion Process: Probability Characteristics Computation and Statistical Analysis," Methodology and Computing in Applied Probability, Springer, vol. 25(2), pages 1-15, June.

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