AI RESEARCH
Deep regression learning from dependent observations with minimum error entropy principle
arXiv CS.LG
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ArXi:2603.11138v1 Announce Type: cross 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.