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

arXiv: https://arxiv.org/abs/2305.06625 · HTML: https://mlsanity.com/paper/2305.06625/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 11 May 2023.

## Builds on (most cited first, 5 of 5)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Regularizing and Optimizing LSTM Language Models](https://mlsanity.com/paper/1708.02182/) (2017)
- [Dropout Training as Adaptive Regularization](https://mlsanity.com/paper/1307.1493/) (2013)
- [The Implicit and Explicit Regularization Effects of Dropout](https://mlsanity.com/paper/2002.12915/) (2020)
- [On the Inductive Bias of Dropout](https://mlsanity.com/paper/1412.4736/) (2014)

## Abstract (arXiv)

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.