mlsanityData to 14 Oct 2026

Deep regression learning from dependent observations with minimum error entropy principle

arXiv 2603.11138 ↗First posted 11 Mar 2026stat.ML cs.LG math.STcited by 0

Not on the map. Why?

Abstract (arXiv)

This paper considers nonparametric regression from strongly mixing observations. The proposed approach is based on deep neural networks with minimum error entropy (MEE) principle. We study two estimators: the non-penalized deep neural network (NPDNN) and the sparse-penalized deep neural network (SPDNN) predictors. Upper bounds of the expected excess risk are established for both estimators over the classes of H"older and composition H"older functions. For the models with Gaussian error, the rates of the upper bound obtained match (up to a logarithmic factor) with the lower bounds established in citeschmidt2020nonparametric, showing that both the MEE-based NPDNN and SPDNN estimators from strongly mixing data can achieve the minimax optimal convergence rate.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 43 references that are in the map. First posted on arXiv 11 Mar 2026.

Builds on

17 of 43 references.

References · 17

  1. 2017Nonparametric regression using deep neural networks with ReLU activation functioncited by 433
  2. 2019Smooth function approximation by deep neural networks with general activation functionscited by 30
  3. 2022How do noise tails impact on deep ReLU networks?cited by 25
  4. 2020Nonconvex sparse regularization for deep neural networks and its optimalitycited by 21
  5. 2020Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfacescited by 17
  6. 2022Theoretical analysis of deep neural networks for temporally dependent observationscited by 16
  7. 2023Classification with Deep Neural Networks and Logistic Losscited by 16
  8. 2020On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent datacited by 14
  9. 2022Adaptive deep learning for nonlinear time series modelscited by 11
  10. 2023Excess risk bound for deep learning under weak dependencecited by 9
  11. 2014Consistency Analysis of an Empirical Minimum Error Entropy Algorithmcited by 7
  12. 2016Learning theory estimates with observations from general stationary stochastic processescited by 7
5 more
  1. 2024Robust deep learning from weakly dependent datacited by 5
  2. 2024Minimax optimality of deep neural networks on dependent data via PAC-Bayes boundscited by 4
  3. 2024Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimalitycited by 3
  4. 2023Deep regression learning with optimal loss functioncited by 2
  5. 2025A general framework for deep learningcited by 2

Built on by

Not cited yet.