mlsanityData to 14 Oct 2026

Sparse-penalized deep neural networks estimator under weak dependence

arXiv 2303.01406 ↗First posted 2 Mar 2023stat.ML cs.LG math.STcited by 8

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Abstract (arXiv)

We consider the nonparametric regression and the classification problems for psi-weakly dependent processes. This weak dependence structure is more general than conditions such as, mixing, association, ldots. A penalized estimation method for sparse deep neural networks is performed. In both nonparametric regression and binary classification problems, we establish oracle inequalities for the excess risk of the sparse-penalized deep neural networks estimators. Convergence rates of the excess risk of these estimators are also derived. The simulation results displayed show that, the proposed estimators overall work well than the non penalized estimators.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 2 Mar 2023.

Builds on

14 of 33 references.

References · 14

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2 more
  1. 2023Excess risk bound for deep learning under weak dependencecited by 9
  2. 2023Deep learning for psi-weakly dependent processescited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 1; 2026: 1.

8 papers.

  1. 2023Implicit Compressibility of Overparametrized Neural Networks Trained with Heavy-Tailed SGDcited by 5
  2. 2024Robust deep learning from weakly dependent datacited by 5
  3. 2024Minimax optimality of deep neural networks on dependent data via PAC-Bayes boundscited by 4
  4. 2024Trade-off Between Dependence and Complexity for Nonparametric Learning -- an Empirical Process Approachcited by 3
  5. 2024Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimalitycited by 3
  6. 2023Penalized deep neural networks estimator with general loss functions under weak dependencecited by 2
  7. 2025A general framework for deep learningcited by 2
  8. 2026Prediction Inference of Time Series with Standard ReLU Deep Neural Networkscited by 0