Valid Inference for L2-Boosting. - David Rügamer; Sonja Greven (CoRR, 2018)

A General Machine Learning Framework for Survival Analysis. - Andreas Bender 001; David Rügamer; Fabian Scheipl; Bernd Bischl (ECML/PKDD (3), 2020)

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

Neural Mixture Distributional Regression. - David Rügamer; Florian Pfisterer; Bernd Bischl (CoRR, 2020)

Inference for L2-Boosting. - David Rügamer; Sonja Greven (Stat. Comput., 2020)

Deep Semi-supervised Learning for Time Series Classification. - Jann Goschenhofer; Rasmus Hvingelby; David Rügamer; Janek Thomas; Moritz Wagner 001; Bernd Bischl (ICMLA, 2021)

Deep Conditional Transformation Models. - Philipp F. M. Baumann; Torsten Hothorn; David Rügamer (ECML/PKDD (3), 2021)

Semi-Structured Deep Piecewise Exponential Models. - Philipp Kopper; Sebastian Pölsterl; Christian Wachinger; Bernd Bischl; Andreas Bender 001; David Rügamer (SPACA, 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)

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)

Learning Statistical Representation with Joint Deep Embedded Clustering. - Mina Rezaei; Emilio Dorigatti; David Rügamer; Bernd Bischl (CoRR, 2021)

Automatic Componentwise Boosting: An Interpretable AutoML System. - Stefan Coors; Daniel Schalk; 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)

Survival-oriented embeddings for improving accessibility to complex data structures. - Tobias Weber; Michael Ingrisch; Matthias Fabritius; Bernd Bischl; 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)

Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach. - Magdalena Mittermeier; Maximilian Weigert; David Rügamer (CoRR, 2021)

Conditional Model Selection in Mixed-Effects Models with cAIC4. - Benjamin Säfken; David Rügamer; Thomas Kneib; Sonja Greven (J. Stat. Softw., 2021)

Joint Debiased Representation Learning and Imbalanced Data Clustering. - Mina Rezaei; Emilio Dorigatti; David Rügamer; Bernd Bischl (ICDM (Workshops), 2022)

Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition. - Felix Ott 001; David Rügamer; Lucas Heublein; Bernd Bischl; Christopher Mutschler (ICPR Workshops (1), 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 001 (STRL@IJCAI, 2022)

Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs. - Tobias Weber; Michael Ingrisch; Bernd Bischl; David Rügamer (MAD@MICCAI, 2022)

Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. - Felix Ott 001; David Rügamer; Lucas Heublein; Bernd Bischl; Christopher Mutschler (ACM Multimedia, 2022)

DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis. - Philipp Kopper; Simon Wiegrebe; Bernd Bischl; Andreas Bender 001; David Rügamer (PAKDD (2), 2022)

Factorized Structured Regression for Large-Scale Varying Coefficient Models. - David Rügamer; Andreas Bender 001; Simon Wiegrebe; Daniel Racek; Bernd Bischl; Christian L. Müller; Clemens Stachl (ECML/PKDD (5), 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 (Teaching ML, 2022)

Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach. - Felix Ott 001; David Rügamer; Lucas Heublein; Bernd Bischl; Christopher Mutschler (WACV, 2022)

Cross-Modal Common Representation Learning with Triplet Loss Functions. - Felix Ott 001; David Rügamer; Lucas Heublein; Bernd Bischl; Christopher Mutschler (CoRR, 2022)

Additive Higher-Order Factorization Machines. - David Rügamer (CoRR, 2022)

Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression. - Felix Ott 001; Nisha Lakshmana Raichur; David Rügamer; Tobias Feigl; Heiko Neumann; Bernd Bischl; Christopher Mutschler (CoRR, 2022)

ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling. - Philipp Schiele; Christoph Berninger; David Rügamer (CoRR, 2022)

Uncertainty-aware predictive modeling for fair data-driven decisions. - Patrick Kaiser; Christoph Kern 001; David Rügamer (CoRR, 2022)

Selective inference for additive and linear mixed models. - David Rügamer; Philipp F. M. Baumann; Sonja Greven (Comput. Stat. Data Anal., 2022)

Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens. - Felix Ott 001; David Rügamer; Lucas Heublein; Tim Hamann; Jens Barth; Bernd Bischl; Christopher Mutschler (Int. J. Document Anal. Recognit., 2022)

Frequentist Uncertainty Quantification in Semi-Structured Neural Networks. - Emilio Dorigatti; Benjamin Schubert; Bernd Bischl; David Rügamer (AISTATS, 2023)

Approximate Bayesian Inference with Stein Functional Variational Gradient Descent. - Tobias Pielok; Bernd Bischl; David Rügamer (ICLR, 2023)

A New PHO-rmula for Improved Performance of Semi-Structured Networks. - David Rügamer (ICML, 2023)

Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. - Tobias Weber; Michael Ingrisch; Bernd Bischl; David Rügamer (PAKDD (3), 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 (ECML/PKDD (1), 2023)

Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition. - Felix Ott 001; David Rügamer; Lucas Heublein; Bernd Bischl; Christopher Mutschler (IEEE Access, 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)

Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models. - Zheyu Zhang 007; Han Yang; Bolei Ma; David Rügamer; Ercong Nie (CoRR, 2023)

Unreading Race: Purging Protected Features from Chest X-ray Embeddings. - Tobias Weber; Michael Ingrisch; Bernd Bischl; David Rügamer (CoRR, 2023)

Challenges in Interpreting Epidemiological Surveillance Data - Experiences from Germany. - Cornelius Fritz; Giacomo De Nicola; Felix Günther 003; David Rügamer; Martje Rave; Marc Schneble; Andreas Bender 001; Maximilian Weigert; Ralph Brinks; Annika Hoyer; Ursula Berger; Helmut Küchenhoff; et al. (J. Comput. Graph. Stat., 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)

Accelerated Componentwise Gradient Boosting Using Efficient Data Representation and Momentum-Based Optimization. - Daniel Schalk; Bernd Bischl; David Rügamer (J. Comput. Graph. Stat., 2023)

Probabilistic time series forecasts with autoregressive transformation models. - David Rügamer; Philipp F. M. Baumann; Thomas Kneib; Torsten Hothorn (Stat. Comput., 2023)

Bayesian Semi-structured Subspace Inference. - Daniel Dold; David Rügamer; Beate Sick; Oliver Dürr (AISTATS, 2024)

Scalable Higher-Order Tensor Product Spline Models. - David Rügamer (AISTATS, 2024)

Position: Why We Must Rethink Empirical Research in Machine Learning. - Moritz Herrmann; F. Julian D. Lange; Katharina Eggensperger; Giuseppe Casalicchio; Marcel Wever; Matthias Feurer; David Rügamer; Eyke Hüllermeier; Anne-Laure Boulesteix; Bernd Bischl (ICML, 2024)

Position: Bayesian Deep Learning is Needed 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; et al. (ICML, 2024)

Generalizing Orthogonalization for Models with Non-Linearities. - David Rügamer; Chris Kolb; Tobias Weber; Lucas Kook; Thomas Nagler (ICML, 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 (ICML, 2024)

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract). - Jonas Gregor Wiese; Lisa Wimmer; Theodore Papamarkou; Bernd Bischl; Stephan Günnemann; David Rügamer (IJCAI, 2024)

A Functional Extension of Semi-Structured Networks. - David Rügamer; Bernard X. W. Liew; Zainab Altai; Almond Stöcker (NeurIPS, 2024)

Team MGTD4ADL at SemEval-2024 Task 8: Leveraging (Sentence) Transformer Models with Contrastive Learning for Identifying Machine-Generated Text. - Huixin Chen; Jan Büssing; David Rügamer; Ercong Nie (SemEval@NAACL, 2024)

How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression. - Lucas Kook; Chris Kolb; Philipp Schiele; Daniel Dold; Marcel Arpogaus; Cornelius Fritz; Philipp F. M. Baumann; Philipp Kopper; Tobias Pielok; Emilio Dorigatti; David Rügamer (UAI, 2024)

Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction. - Tobias Weber; Michael Ingrisch; Bernd Bischl; David Rügamer (WACV, 2024)

Interpretable Machine Learning for TabPFN. - David Rundel; Julius Kobialka; Constantin von Crailsheim; Matthias Feurer; Thomas Nagler; David Rügamer (xAI (2), 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; et al. (CoRR, 2024)

Training Survival Models using Scoring Rules. - Philipp Kopper; David Rügamer; Raphael Sonabend; Bernd Bischl; Andreas Bender 001 (CoRR, 2024)

Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition. - Tobias Weber; Jakob Dexl; David Rügamer; Michael Ingrisch (CoRR, 2024)

Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects. - David Köhler; David Rügamer; Matthias Schmid (CoRR, 2024)

Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo. - Lucas Kook; Philipp F. M. Baumann; Oliver Dürr; Beate Sick; David Rügamer (J. Stat. Softw., 2024)

Fusing structure from motion and simulation-augmented pose regression from optical flow for challenging indoor environments. - Felix Ott 001; Lucas Heublein; David Rügamer; Bernd Bischl; Christopher Mutschler (J. Vis. Commun. Image Represent., 2024)

Interpretable Additive Tabular Transformer Networks. - Anton Frederik Thielmann; Arik Reuter; Thomas Kneib; David Rügamer; Benjamin Säfken (Trans. Mach. Learn. Res., 2024)

Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models. - Daniel Schalk; Bernd Bischl; David Rügamer (Stat. Comput., 2024)

Paths and Ambient Spaces in Neural Loss Landscapes. - Daniel Dold; Julius Kobialka; Nicolai Palm; Emanuel Sommer; David Rügamer; Oliver Dürr (AISTATS, 2025)

Additive Model Boosting: New Insights and Path(ologie)s. - Rickmer Schulte; David Rügamer (AISTATS, 2025)

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries. - Chris Kolb; Tobias Weber; Bernd Bischl; David Rügamer (ICLR, 2025)

Calibrating LLMs with Information-Theoretic Evidential Deep Learning. - Yawei Li; David Rügamer; Bernd Bischl; Mina Rezaei (ICLR, 2025)

Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks. - Emanuel Sommer; Jakob Robnik; Giorgi Nozadze; Uros Seljak; David Rügamer (ICLR, 2025)

Preventing Sensitive Information Leakage via Post-hoc Orthogonalization with Application to Chest Radiograph Embeddings. - Tobias Weber; Michael Ingrisch; Bernd Bischl; David Rügamer (PAKDD (4), 2025)

Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals. - Marcel Arpogaus; Thomas Kneib; Thomas Nagler; David Rügamer (UAI, 2025)

Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data. - Nandhini Santhanam; Hee E. Kim; David Rügamer; Andreas Bender; Stefan Muthers; Chang Gyu Cho; Angelika Alonso; Kristina Szabo; Franz-Simon Centner; Holger Wenz; Thomas Ganslandt; Michael Platten; et al. (npj Digit. Medicine, 2025)

Can Transformers Learn Full Bayesian Inference in Context? - Arik Reuter; Tim G. J. Rudner; Vincent Fortuin; David Rügamer (CoRR, 2025)

How To Make Your Cell Tracker Say "I dunno!". - Richard D. Paul; Johannes Seiffarth; David Rügamer; Hanno Scharr; Katharina Nöh (CoRR, 2025)

Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors. - Thomas Nagler; David Rügamer (CoRR, 2025)

An Interpretable Representation Learning Approach for Diffusion Tensor Imaging. - Vishwa Mohan Singh; Alberto Gaston Villagran Asiares; Luisa Sophie Schuhmacher; Kate Rendall; Simon Weißbrod; David Rügamer; Inga Koerte (CoRR, 2025)

Position: The Future of Bayesian Prediction Is Prior-Fitted. - Samuel Müller 005; Arik Reuter; Noah Hollmann; David Rügamer; Frank Hutter (CoRR, 2025)

Revisiting Unbiased Implicit Variational Inference. - Tobias Pielok; Bernd Bischl; David Rügamer (CoRR, 2025)

Semi-Implicit Variational Inference via Kernelized Path Gradient Descent. - Tobias Pielok; Bernd Bischl; David Rügamer (CoRR, 2025)

Enhancing Traffic Accident Classifications: Application of NLP Methods for City Safety. - Enes Özeren; Alexander Ulbrich; Sascha Filimon; David Rügamer; Andreas Bender (CoRR, 2025)

Adjustment for Confounding using Pre-Trained Representations. - Rickmer Schulte; David Rügamer; Thomas Nagler (CoRR, 2025)