5 papers · 1 filter
Adaptive deep nonparametric regression from dependent data under covariate shift
William Kengne, Ehud Mossa Ockegna
Covariate shift often occurs because, in many real applications, the source and the target observations may be generated from different distributions. In this case, the standard me…
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.…
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…
Minimax optimality of deep neural networks on dependent data via PAC-Bayes bounds
Pierre Alquier, William Kengne
In a groundbreaking work, Schmidt-Hieber (2020) proved the minimax optimality of deep neural networks with ReLu activation for least-square regression estimation over a large class…
Robust deep learning from weakly dependent data
William Kengne, Modou Wade
Recent developments on deep learning established some theoretical properties of deep neural networks estimators. However, most of the existing works on this topic are restricted to…