Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families

作     者：Schwienhorst, Benedikt Lütke Kock, Lucas Klein, Nadja Nott, David J. 

作者机构：Department of Mathematics University of Hamburg Germany Department of Statistics and Data Science National University of Singapore Singapore Research Center Trustworthy Data Science and Security Department of Statistics Technische Universität Dortmund Germany 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Splines 

摘      要：Even though dropout is a popular regularization technique, its theoretical properties are not fully understood. In this paper we study dropout regularization in extended generalized linear models based on double exponential families, for which the dispersion parameter can vary with the features. A theoretical analysis shows that dropout regularization prefers rare but important features in both the mean and dispersion, generalizing an earlier result for conventional generalized linear models. To illustrate, we apply dropout to adaptive smoothing with B-splines, where both the mean and dispersion parameters are modeled flexibly. The important B-spline basis functions can be thought of as rare features, and we confirm in experiments that dropout is an effective form of regularization for mean and dispersion parameters that improves on a penalized maximum likelihood approach with an explicit smoothness penalty. An application to traffic detection data from Berlin further illustrates the benefits of our method. Copyright © 2023, The Authors. All rights reserved.