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Deep and interpretable regression models for ordinal outcomes
v1v2v3v4 (latest)

Deep and interpretable regression models for ordinal outcomes

16 October 2020
Lucas Kook
L. Herzog
Torsten Hothorn
Oliver Durr
Beate Sick
ArXiv (abs)PDFHTML

Papers citing "Deep and interpretable regression models for ordinal outcomes"

7 / 7 papers shown
Title
Single-shot Bayesian approximation for neural networks
Single-shot Bayesian approximation for neural networks
K. Brach
Beate Sick
Oliver Durr
BDLUQCV
29
0
0
24 Aug 2023
A New PHO-rmula for Improved Performance of Semi-Structured Networks
A New PHO-rmula for Improved Performance of Semi-Structured Networks
David Rügamer
55
10
0
01 Jun 2023
mixdistreg: An R Package for Fitting Mixture of Experts Distributional
  Regression with Adaptive First-order Methods
mixdistreg: An R Package for Fitting Mixture of Experts Distributional Regression with Adaptive First-order Methods
David Rügamer
75
0
0
04 Feb 2023
Deep interpretable ensembles
Deep interpretable ensembles
Lucas Kook
Andrea Götschi
Philipp F. M. Baumann
Torsten Hothorn
Beate Sick
UQCV
58
9
0
25 May 2022
Distributional Gradient Boosting Machines
Distributional Gradient Boosting Machines
Alexander März
Thomas Kneib
AI4CE
66
7
0
02 Apr 2022
Transformation Models for Flexible Posteriors in Variational Bayes
Transformation Models for Flexible Posteriors in Variational Bayes
Sefan Hörtling
Daniel Dold
Oliver Durr
Beate Sick
40
0
0
01 Jun 2021
Deep Conditional Transformation Models
Deep Conditional Transformation Models
Philipp F. M. Baumann
Torsten Hothorn
David Rügamer
46
29
0
15 Oct 2020
1