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

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

1 April 2020

Torsten Hothorn

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

19 / 19 papers shown

Title
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals Marcel Arpogaus Thomas Kneib Thomas Nagler David Rügamer 46 0 0 20 May 2025
Interpretable Neural Causal Models with TRAM-DAGs Beate Sick Oliver Durr CML 78 1 0 20 Mar 2025
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression Lucas Kook Chris Kolb Philipp Schiele Daniel Dold Marcel Arpogaus ... Philipp F. M. Baumann Philipp Kopper Tobias Pielok Emilio Dorigatti David Rügamer BDL AI4TS 74 2 0 08 May 2024
Bayesian Semi-structured Subspace Inference Daniel Dold David Rügamer Beate Sick Oliver Durr BDL 53 1 0 23 Jan 2024
Single-shot Bayesian approximation for neural networks K. Brach Beate Sick Oliver Durr BDL UQCV 29 0 0 24 Aug 2023
A New PHO-rmula for Improved Performance of Semi-Structured Networks David Rügamer 55 10 0 01 Jun 2023
An interpretable neural network-based non-proportional odds model for ordinal regression Akifumi Okuno Kazuharu Harada 63 1 0 31 Mar 2023
Estimating Conditional Distributions with Neural Networks using R package deeptrafo Lucas Kook Philipp F. M. Baumann Oliver Durr Beate Sick David Rügamer 58 6 0 24 Nov 2022
Deep conditional transformation models for survival analysis Gabriele Campanella Lucas Kook I. Häggström Torsten Hothorn Thomas J. Fuchs 43 2 0 20 Oct 2022
Sample-based Uncertainty Quantification with a Single Deterministic Neural Network T. Kanazawa Chetan Gupta UQCV 79 4 0 17 Sep 2022
Deep interpretable ensembles Lucas Kook Andrea Götschi Philipp F. M. Baumann Torsten Hothorn Beate Sick UQCV 58 9 0 25 May 2022
Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows M. Arpogaus Marcus Voss Beate Sick Mark Nigge-Uricher Oliver Durr 65 18 0 29 Apr 2022
Distributional Gradient Boosting Machines Alexander März Thomas Kneib AI4CE 66 7 0 02 Apr 2022
Bernstein Flows for Flexible Posteriors in Variational Bayes Oliver Durr Stephan Hörling Daniel Dold Ivonne Kovylov Beate Sick BDL 86 4 0 11 Feb 2022
Uncertainty estimation under model misspecification in neural network regression Maria R. Cervera Rafael Dätwyler Francesco DÁngelo Hamza Keurti Benjamin Grewe Christian Henning 58 6 0 23 Nov 2021
Probabilistic Time Series Forecasts with Autoregressive Transformation Models David Rügamer Philipp F. M. Baumann Thomas Kneib Torsten Hothorn AI4TS 104 13 0 15 Oct 2021
Transformation Models for Flexible Posteriors in Variational Bayes Sefan Hörtling Daniel Dold Oliver Durr Beate Sick 40 0 0 01 Jun 2021
Deep and interpretable regression models for ordinal outcomes Lucas Kook L. Herzog Torsten Hothorn Oliver Durr Beate Sick 62 15 0 16 Oct 2020
Deep Conditional Transformation Models Philipp F. M. Baumann Torsten Hothorn David Rügamer 46 29 0 15 Oct 2020