138 citations · 302 across the 32 of their papers we have counts for
6 papers · 1 filter
The Deep Promotion Time Cure Model
Victor Medina-Olivares, Stefan Lessmann, Nadja Klein
We propose a novel method for predicting time-to-event in the presence of cure fractions based on flexible survivals models integrated into a deep neural network framework. Our app…
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families
Benedikt Lütke Schwienhorst, Lucas Kock, Nadja Klein +1
Even though dropout is a popular regularization technique, its theoretical properties are not fully understood. In this paper we study dropout regularization in extended generalize…
Marginally calibrated response distributions for end-to-end learning in autonomous driving
Clara Hoffmann, Nadja Klein
End-to-end learners for autonomous driving are deep neural networks that predict the instantaneous steering angle directly from images of the ahead-lying street. These learners mus…
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
David Rügamer, Chris Kolb, Cornelius Fritz +11
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of…
Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities
David Rügamer, Chris Kolb, Nadja Klein
Combining additive models and neural networks allows to broaden the scope of statistical regression and extend deep learning-based approaches by interpretable structured additive p…
Boosting Joint Models for Longitudinal and Time-to-Event Data
Elisabeth Waldmann, David Taylor-Robinson, Nadja Klein +4
Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in cli…