Closing the Loop: Motion Prediction Models beyond Open-Loop Benchmarks.
CoRR, May, 2025
Density regression via Dirichlet process mixtures of normal structured additive regression models.
Stat. Comput., April, 2025
Anisotropic multidimensional smoothing using Bayesian tensor product P-splines.
Stat. Comput., April, 2025
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification.
CoRR, April, 2025
Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection.
Trans. Mach. Learn. Res., 2025
WeedsGalore: A Multispectral and Multitemporal UAV-Based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2025
The Deep Promotion Time Cure Model.
IEEE Trans. Neural Networks Learn. Syst., December, 2024
From Counting Stations to City-Wide Estimates: Data-Driven Bicycle Volume Extrapolation.
CoRR, 2024
Boosting Causal Additive Models.
CoRR, 2024
Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection.
Proceedings of the Uncertainty in Artificial Intelligence, 2024
Dropout Regularization in Extended Generalized Linear Models Based on Double Exponential Families.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024
Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024
Informed Spectral Normalized Gaussian Processes for Trajectory Prediction.
Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024
Investigating Calibration and Corruption Robustness of Post-hoc Pruned Perception CNNs: An Image Classification Benchmark Study.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
Learning Causal Graphs in Manufacturing Domains Using Structural Equation Models.
Int. J. Semantic Comput., December, 2023
Correction to : Variational inference and sparsity in high-dimensional deep Gaussian mixture models.
Stat. Comput., 2023
deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression.
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J. Stat. Softw., 2023
Informed Priors for Knowledge Integration in Trajectory Prediction.
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.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023
A non-stationary model for spatially dependent circular response data based on wrapped Gaussian processes.
Stat. Comput., 2022
Variational inference and sparsity in high-dimensional deep Gaussian mixture models.
Stat. Comput., 2022
Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study.
Entropy, 2022
Correcting for sample selection bias in Bayesian distributional regression models.
Comput. Stat. Data Anal., 2022
bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond).
J. Stat. Softw., 2021
Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance.
J. Comput. Graph. Stat., 2021
Marginally Calibrated Deep Distributional Regression.
J. Comput. Graph. Stat., 2021
Marginally calibrated response distributions for end-to-end learning in autonomous driving.
CoRR, 2021
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression.
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CoRR, 2021
A Unifying Network Architecture for Semi-Structured Deep Distributional Learning.
CoRR, 2020
Multivariate effect priors in bivariate semiparametric recursive Gaussian models.
Comput. Stat. Data Anal., 2019
Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression.
Environ. Model. Softw., 2018
Simultaneous inference in structured additive conditional copula regression models: a unifying Bayesian approach.
Stat. Comput., 2016