2 citations · 3 across the 9 of their papers we have counts for
9 papers
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.…
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…
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…
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…
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…
Penalized deep neural networks estimator with general loss functions under weak dependence
William Kengne, Modou Wade
This paper carries out sparse-penalized deep neural networks predictors for learning weakly dependent processes, with a broad class of loss functions. We deal with a general framew…
Excess risk bound for deep learning under weak dependence
William Kengne
This paper considers deep neural networks for learning weakly dependent processes in a general framework that includes, for instance, regression estimation, time series prediction,…
Deep learning for -weakly dependent processes
William Kengne, Wade Modou
In this paper, we perform deep neural networks for learning -weakly dependent processes. Such weak-dependence property includes a class of weak dependence conditions such as mix…