3 papers
stat.ML2026
Deep regression learning from dependent observations with minimum error entropy principle
William Kengne, Modou Wade
This paper considers nonparametric regression from strongly mixing observations. The proposed approach is based on deep neural networks with minimum error entropy (MEE) principle.…
math.ST2025
A general framework for deep learning
William Kengne, Modou Wade
This paper develops a general approach for deep learning for a setting that includes nonparametric regression and classification. We perform a framework from data that fulfills a g…
stat.ML2025
Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimality
William Kengne, Modou Wade
The explicit regularization and optimality of deep neural networks estimators from independent data have made considerable progress recently. The study of such properties on depend…