Ehud Mossa Ockegna

Adaptive deep nonparametric regression from dependent data under covariate shift

This paper considers deep neural network estimators for nonparametric quantile and Huber regression under covariate shift and from dependent observations and proposes a sparse-penalized deep neural network (SPDNN) estimator that takes into account the discrepancy between the source and target distributions of the data.