David Rügamer

According to our database, David Rügamer authored at least 54 papers between 2018 and 2024.

Collaborative distances:

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

2024

Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models.

Daniel Schalk, Bernd Bischl, David Rügamer

Stat. Comput., February, 2024

Training Survival Models using Scoring Rules.

Philipp Kopper, David Rügamer, Raphael Sonabend, Bernd Bischl, Andreas Bender

CoRR, 2024

Interpretable Machine Learning for TabPFN.

David Rundel, Julius Kobialka, Constantin von Crailsheim, Matthias Feurer, Thomas Nagler, David Rügamer

CoRR, 2024

Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?

Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer

CoRR, 2024

Scalable Higher-Order Tensor Product Spline Models.

David Rügamer

CoRR, 2024

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.

Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

CoRR, 2024

Bayesian Semi-structured Subspace Inference.

Daniel Dold, David Rügamer, Beate Sick, Oliver Dürr

CoRR, 2024

Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction.

Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

2023

Probabilistic time series forecasts with autoregressive transformation models.

David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn

Stat. Comput., April, 2023

Accelerated Componentwise Gradient Boosting Using Efficient Data Representation and Momentum-Based Optimization.

Daniel Schalk, Bernd Bischl, David Rügamer

J. Comput. Graph. Stat., April, 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

Unreading Race: Purging Protected Features from Chest X-ray Embeddings.

Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer

CoRR, 2023

Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models.

Zheyu Zhang, Han Yang, Bolei Ma, David Rügamer, Ercong Nie

CoRR, 2023

Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization.

Chris Kolb, Christian L. Müller, Bernd Bischl, David Rügamer

CoRR, 2023

Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments.

Felix Ott, Lucas Heublein, David Rügamer, Bernd Bischl, Christopher Mutschler

CoRR, 2023

Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition.

Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler

IEEE Access, 2023

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry.

Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer

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

Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis.

Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2023

A New PHO-rmula for Improved Performance of Semi-Structured Networks.

David Rügamer

Proceedings of the International Conference on Machine Learning, 2023

Approximate Bayesian Inference with Stein Functional Variational Gradient Descent.

Tobias Pielok, Bernd Bischl, David Rügamer

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Frequentist Uncertainty Quantification in Semi-Structured Neural Networks.

Emilio Dorigatti, Benjamin Schubert, Bernd Bischl, David Rügamer

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens.

Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler

Int. J. Document Anal. Recognit., 2022

Selective inference for additive and linear mixed models.

David Rügamer, Philipp F. M. Baumann, Sonja Greven

Comput. Stat. Data Anal., 2022

Uncertainty-aware predictive modeling for fair data-driven decisions.

Patrick Kaiser, Christoph Kern, David Rügamer

CoRR, 2022

ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling.

Philipp Schiele, Christoph Berninger, David Rügamer

CoRR, 2022

Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression.

Felix Ott, Nisha Lakshmana Raichur, David Rügamer, Tobias Feigl, Heiko Neumann, Bernd Bischl, Christopher Mutschler

CoRR, 2022

Additive Higher-Order Factorization Machines.

David Rügamer

CoRR, 2022

Cross-Modal Common Representation Learning with Triplet Loss Functions.

Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler

CoRR, 2022

Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach.

Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

Developing Open Source Educational Resources for Machine Learning and Data Science.

Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl

Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, 2022

Factorized Structured Regression for Large-Scale Varying Coefficient Models.

David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis.

Philipp Kopper, Simon Wiegrebe, Bernd Bischl, Andreas Bender, David Rügamer

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2022

Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift.

Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler

Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022

Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs.

Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer

Proceedings of the Medical Applications with Disentanglements - First MICCAI Workshop, 2022

Uncertainty-aware Evaluation of Time-series Classification for Online Handwriting Recognition with Domain Shift.

Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott

Proceedings of the 1st International Workshop on Spatio-Temporal Reasoning and Learning (STRL 2022) co-located with the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI 2022, 2022

Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition.

Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler

Proceedings of the Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges, 2022

Joint Debiased Representation Learning and Imbalanced Data Clustering.

Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl

Proceedings of the IEEE International Conference on Data Mining Workshops, 2022

2021

Conditional Model Selection in Mixed-Effects Models with cAIC4.

Benjamin Säfken, David Rügamer, Thomas Kneib, Sonja Greven

J. Stat. Softw., 2021

Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach.

Magdalena Mittermeier, Maximilian Weigert, David Rügamer

CoRR, 2021

Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation.

Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer

CoRR, 2021

Survival-oriented embeddings for improving accessibility to complex data structures.

Tobias Weber, Michael Ingrisch, Matthias Fabritius, Bernd Bischl, David Rügamer

CoRR, 2021

Transforming Autoregression: Interpretable and Expressive Time Series Forecast.

David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn

CoRR, 2021

Automatic Componentwise Boosting: An Interpretable AutoML System.

Stefan Coors, Daniel Schalk, Bernd Bischl, David Rügamer

CoRR, 2021

Learning Statistical Representation with Joint Deep Embedded Clustering.

Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl

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

Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany.

Cornelius Fritz, Emilio Dorigatti, David Rügamer

CoRR, 2021

Semi-Structured Deep Piecewise Exponential Models.

Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer

Proceedings of AAAI Symposium on Survival Prediction, 2021

Deep Conditional Transformation Models.

Philipp F. M. Baumann, Torsten Hothorn, David Rügamer

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Deep Semi-supervised Learning for Time Series Classification.

Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl

Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

2020

Inference for L₂-Boosting.

David Rügamer, Sonja Greven

Stat. Comput., 2020

Neural Mixture Distributional Regression.

David Rügamer, Florian Pfisterer, Bernd Bischl

CoRR, 2020

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

David Rügamer, Chris Kolb, Nadja Klein

CoRR, 2020

A General Machine Learning Framework for Survival Analysis.

Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

2018

Valid Inference for L₂-Boosting.

David Rügamer, Sonja Greven

CoRR, 2018