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A generalization bound of deep neural networks for dependent data

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  • Do, Quan Huu
  • Nguyen, Binh T.
  • Ho, Lam Si Tung

Abstract

Existing generalization bounds for deep neural networks require data to be independent and identically distributed (iid). This assumption may not hold in real-life applications such as evolutionary biology, infectious disease epidemiology, and stock price prediction. This work establishes a generalization bound of feed-forward neural networks for non-stationary φ-mixing data.

Suggested Citation

  • Do, Quan Huu & Nguyen, Binh T. & Ho, Lam Si Tung, 2024. "A generalization bound of deep neural networks for dependent data," Statistics & Probability Letters, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:stapro:v:208:y:2024:i:c:s0167715224000294
    DOI: 10.1016/j.spl.2024.110060
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    References listed on IDEAS

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    1. Truquet, Lionel, 2023. "Strong mixing properties of discrete-valued time series with exogenous covariates," Stochastic Processes and their Applications, Elsevier, vol. 160(C), pages 294-317.
    2. James B. Heaton & Nicholas Polson & Jan H. Witte, 2017. "Rejoinder to ‘Deep learning for finance: deep portfolios’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 19-21, January.
    3. Ho, Lam Si Tung & Dinh, Vu, 2022. "Searching for minimal optimal neural networks," Statistics & Probability Letters, Elsevier, vol. 183(C).
    4. White, Halbert & Domowitz, Ian, 1984. "Nonlinear Regression with Dependent Observations," Econometrica, Econometric Society, vol. 52(1), pages 143-161, January.
    5. J. B. Heaton & N. G. Polson & J. H. Witte, 2017. "Deep learning for finance: deep portfolios," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 3-12, January.
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