Nadja Klein

According to our database, Nadja Klein authored at least 22 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Informed Spectral Normalized Gaussian Processes for Trajectory Prediction.

Christian Schlauch, Christian Wirth, Nadja Klein

CoRR, 2024

Boosting Causal Additive Models.

Maximilian Kertel, Nadja Klein

CoRR, 2024

2023

Learning Causal Graphs in Manufacturing Domains Using Structural Equation Models.

Maximilian Kertel, Stefan Harmeling, Markus Pauly, Nadja Klein

Int. J. Semantic Comput., December, 2023

Correction to : Variational inference and sparsity in high-dimensional deep Gaussian mixture models.

Lucas Kock, Nadja Klein, David J. Nott

Stat. Comput., 2023

deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression.

David Rügamer, Chris Kolb, Cornelius Fritz, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Philipp F. M. Baumann, Lucas Kook, Nadja Klein, Christian L. Müller

J. Stat. Softw., 2023

The Deep Promotion Time Cure Model.

Victor Medina-Olivares, Stefan Lessmann, Nadja Klein

CoRR, 2023

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

Benedikt Lütke Schwienhorst, Lucas Kock, David J. Nott, Nadja Klein

CoRR, 2023

Informed Priors for Knowledge Integration in Trajectory Prediction.

Christian Schlauch, Christian Wirth, Nadja Klein

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Semantic Segmentation of Crops and Weeds with Probabilistic Modeling and Uncertainty Quantification.

Ekin Celikkan, Mohammadmehdi Saberioon, Martin Herold, Nadja Klein

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

A non-stationary model for spatially dependent circular response data based on wrapped Gaussian processes.

Isa Marques, Thomas Kneib, Nadja Klein

Stat. Comput., 2022

Variational inference and sparsity in high-dimensional deep Gaussian mixture models.

Lucas Kock, Nadja Klein, David J. Nott

Stat. Comput., 2022

Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study.

Lorena Hafermann, Nadja Klein, Géraldine Rauch, Michael Kammer, Georg Heinze

Entropy, 2022

Correcting for sample selection bias in Bayesian distributional regression models.

Paul F. V. Wiemann, Nadja Klein, Thomas Kneib

Comput. Stat. Data Anal., 2022

2021

bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond).

Nikolaus Umlauf, Nadja Klein, Thorsten Simon, Achim Zeileis

J. Stat. Softw., 2021

Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance.

Xuejun Yu, David J. Nott, Minh-Ngoc Tran, Nadja Klein

J. Comput. Graph. Stat., 2021

Marginally Calibrated Deep Distributional Regression.

Nadja Klein, David J. Nott, Michael Stanley Smith

J. Comput. Graph. Stat., 2021

Marginally calibrated response distributions for end-to-end learning in autonomous driving.

Clara Hoffmann, Nadja Klein

CoRR, 2021

deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression.

David Rügamer, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, Nadja Klein, Chris Kolb, Florian Pfisterer, Philipp Kopper, Bernd Bischl, Christian L. Müller

CoRR, 2021

2020

A Unifying Network Architecture for Semi-Structured Deep Distributional Learning.

David Rügamer, Chris Kolb, Nadja Klein

CoRR, 2020

2019

Multivariate effect priors in bivariate semiparametric recursive Gaussian models.

Hauke Thaden, Nadja Klein, Thomas Kneib

Comput. Stat. Data Anal., 2019

2018

Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression.

Laura Ríos-Pena, Thomas Kneib, Carmen Cadarso-Suárez, Nadja Klein, Manuel Marey-Pérez

Environ. Model. Softw., 2018

2016

Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach.

Nadja Klein, Thomas Kneib

Stat. Comput., 2016