Deep Conditional Transformation Models

10/15/2020
by   Philipp F. M. Baumann, et al.
25

Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set of features remains challenging, especially in high-dimensional settings. Conditional transformation models provide a semi-parametric approach that allows to model a large class of conditional CDFs without an explicit parametric distribution assumption and with only a few parameters. Existing estimation approaches within the class of transformation models are, however, either limited in their complexity and applicability to unstructured data sources such as images or text, or can incorporate complex effects of different features but lack interpretability. We close this gap by introducing the class of deep conditional transformation models which unify existing approaches and allow to learn both interpretable (non-)linear model terms and more complex predictors in one holistic neural network. To this end we propose a novel network architecture, provide details on different model definitions and derive suitable constraints and derive suitable network regularization terms. We demonstrate the efficacy of our approach through numerical experiments and applications.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 6

01/09/2017

Transformation Forests

Regression models for supervised learning problems with a continuous tar...
10/16/2020

Ordinal Neural Network Transformation Models: Deep and interpretable regression models for ordinal outcomes

Outcomes with a natural order commonly occur in prediction tasks and oft...
04/01/2020

Deep transformation models: Tackling complex regression problems with neural network based transformation models

We present a deep transformation model for probabilistic regression. Dee...
01/14/2018

Non-Parametric Transformation Networks

ConvNets have been very effective in many applications where it is requi...
11/01/2020

DebiNet: Debiasing Linear Models with Nonlinear Overparameterized Neural Networks

Recent years have witnessed strong empirical performance of over-paramet...
02/13/2020

A Unifying Network Architecture for Semi-Structured Deep Distributional Learning

We propose a unifying network architecture for deep distributional learn...
02/12/2022

DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis

Survival analysis (SA) is an active field of research that is concerned ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.