Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach. - Nadja Klein; Thomas Kneib (Stat. Comput., 2016)

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)

Multivariate effect priors in bivariate semiparametric recursive Gaussian models. - Hauke Thaden; Nadja Klein; Thomas Kneib (Comput. Stat. Data Anal., 2019)

A Unifying Network Architecture for Semi-Structured Deep Distributional Learning. - David Rügamer; Chris Kolb; Nadja Klein (CoRR, 2020)

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)

Marginally calibrated response distributions for end-to-end learning in autonomous driving. - Clara Hoffmann; Nadja Klein (CoRR, 2021)

Marginally Calibrated Deep Distributional Regression. - Nadja Klein; David J. Nott; Michael Stanley Smith (J. Comput. Graph. Stat., 2021)

Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance. - Xuejun Yu 002; David J. Nott; Minh-Ngoc Tran; Nadja Klein (J. Comput. Graph. Stat., 2021)

bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond). - Nikolaus Umlauf; Nadja Klein; Thorsten Simon; Achim Zeileis (J. Stat. Softw., 2021)

Correcting for sample selection bias in Bayesian distributional regression models. - Paul F. V. Wiemann; Nadja Klein; Thomas Kneib (Comput. Stat. Data Anal., 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)

Variational inference and sparsity in high-dimensional deep Gaussian mixture models. - Lucas Kock; Nadja Klein; David J. Nott (Stat. Comput., 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)

Semantic Segmentation of Crops and Weeds with Probabilistic Modeling and Uncertainty Quantification. - Ekin Celikkan; Mohammadmehdi Saberioon; Martin Herold 001; Nadja Klein (ICCV (Workshops), 2023)

Informed Priors for Knowledge Integration in Trajectory Prediction. - Christian Schlauch; Christian Wirth 001; Nadja Klein (ECML/PKDD (5), 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; et al. (J. Stat. Softw., 2023)

Correction to : Variational inference and sparsity in high-dimensional deep Gaussian mixture models. - Lucas Kock; Nadja Klein; David J. Nott (Stat. Comput., 2023)

Learning Causal Graphs in Manufacturing Domains Using Structural Equation Models. - Maximilian Kertel; Stefan Harmeling; Markus Pauly; Nadja Klein (Int. J. Semantic Comput., 2023)

Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study. - Pallavi Mitra; Gesina Schwalbe; Nadja Klein (CVPR Workshops, 2024)

Informed Spectral Normalized Gaussian Processes for Trajectory Prediction. - Christian Schlauch; Christian Wirth 001; Nadja Klein (ECAI, 2024)

Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation. - Paulo Yanez Sarmiento; Simon Witzke; Nadja Klein; Bernhard Y. Renard (ECML/PKDD (4), 2024)

Dropout Regularization in Extended Generalized Linear Models Based on Double Exponential Families. - Benedikt Lütke Schwienhorst; Lucas Kock; Nadja Klein; David J. Nott (ECML/PKDD (6), 2024)

Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection. - Moussa Kassem Sbeyti; Michelle Karg; Christian Wirth 001; Nadja Klein; Sahin Albayrak (UAI, 2024)

Boosting Causal Additive Models. - Maximilian Kertel; Nadja Klein (CoRR, 2024)

From Counting Stations to City-Wide Estimates: Data-Driven Bicycle Volume Extrapolation. - Silke K. Kaiser; Nadja Klein; Lynn H. Kaack (CoRR, 2024)

The Deep Promotion Time Cure Model. - Victor Medina-Olivares; Stefan Lessmann; Nadja Klein (IEEE Trans. Neural Networks Learn. Syst., 2024)

MultiFlags: A Probabilistic Framework for Article-Based Size Advice in Fashion E-Commerce. - Matthias Späth; Andrea Nestler; Henry Böddeker; Leonidas Lefakis; Yevgeniy Puzikov; Rodrigo Weffer; Nour Karessli; Nadja Klein; Reza Shirvany (IC3K (KDIR), 2025)

WeedsGalore: A Multispectral and Multitemporal UAV-Based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields. - Ekin Celikkan; Timo Kunzmann; Yertay Yeskaliyev; Sibylle Itzerott; Nadja Klein; Martin Herold 001 (WACV, 2025)

Scalable Estimation for Structured Additive Distributional Regression. - Nikolaus Umlauf; Johannes Seiler; Mattias Wetscher; Thorsten Simon; Stefan Lang 003; Nadja Klein (J. Comput. Graph. Stat., 2025)

Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection. - Moussa Kassem Sbeyti; Nadja Klein; Azarm Nowzad; Fikret Sivrikaya; Sahin Albayrak (Trans. Mach. Learn. Res., 2025)

Beware of "Explanations" of AI. - David Martens; Galit Shmueli; Theodoros Evgeniou; Kevin Bauer; Christian Janiesch; Stefan Feuerriegel; Sebastian Gabel; Sofie Goethals; Travis Greene; Nadja Klein; Mathias Kraus; Niklas Kühl 001; et al. (CoRR, 2025)

Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification. - Kumar Manas; Christian Schlauch; Adrian Paschke; Christian Wirth 001; Nadja Klein (CoRR, 2025)

Anisotropic multidimensional smoothing using Bayesian tensor product P-splines. - Paul Bach; Nadja Klein (Stat. Comput., 2025)

Density regression via Dirichlet process mixtures of normal structured additive regression models. - María Xosé Rodríguez-Álvarez; Vanda Inácio; Nadja Klein (Stat. Comput., 2025)

Closing the Loop: Motion Prediction Models beyond Open-Loop Benchmarks. - Mohamed-Khalil Bouzidi; Christian Schlauch; Nicole Scheuerer; Yue Yao; Nadja Klein; Daniel Göhring; Jörg Reichardt (CoRR, 2025)

Generative AI for Autonomous Driving: A Review. - Katharina Winter; Abhishek Vivekanandan; Rupert Polley; Yinzhe Shen; Christian Schlauch; Mohamed-Khalil Bouzidi; Bojan Derajic; Natalie Grabowsky; Annajoyce Mariani; Dennis Rochau; Giovanni Lucente; Harsh Yadav; et al. (CoRR, 2025)

Streamlining the Development of Active Learning Methods in Real-World Object Detection. - Moussa Kassem Sbeyti; Nadja Klein; Michelle Karg; Christian Wirth 001; Sahin Albayrak (CoRR, 2025)

Amortized Variational Inference for Partial-Label Learning: A Probabilistic Approach to Label Disambiguation. - Tobias Fuchs; Nadja Klein (CoRR, 2025)