David Rügamer

129 papers A* 15A 9C 2Journal 85Unranked 18
YearRankTypeTitle / Venue / Authors
2026 J jnl
Can Microcanonical Langevin Dynamics Leverage Mini-Batch Gradient Noise?
CoRR
Emanuel Sommer, Kangning Diao, Jakob Robnik, Uros Seljak, David Rügamer
2026 J jnl
Deep generative models in digital subtraction angiography (DSA) and X-ray angiography: a systematic review.
Artif. Intell. Rev.
Qiwen Xu, David Rügamer, Andreas Bender, Máté E. Maros
2026 J jnl
On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors.
CoRR
Julius Kobialka, Emanuel Sommer, Chris Kolb, Juntae Kwon, Daniel Dold, David Rügamer
2026 J jnl
Semantically Conditioned Diffusion Models for Cerebral DSA Synthesis.
CoRR
Qiwen Xu, David Rügamer, Holger Wenz, Johann Fontana, Nora Meggyeshazi, Andreas Bender, Máté E. Maros
2026 J jnl
Smoothing the Edges: Smooth Optimization for Sparse Regularization Using Hadamard Overparametrization.
Mach. Learn.
Chris Kolb, Christian L. Müller, Bernd Bischl, David Rügamer
2025 A conf
Additive Model Boosting: New Insights and Path(ologie)s.
AISTATS
Rickmer Schulte, David Rügamer
2025 J jnl
Additive Model Boosting: New Insights and Path(ologie)s.
CoRR
Rickmer Schulte, David Rügamer
2025 A* conf
Adjustment for Confounding using Pre-Trained Representations.
ICML
Rickmer Schulte, David Rügamer, Thomas Nagler
2025 J jnl
Adjustment for Confounding using Pre-Trained Representations.
CoRR
Rickmer Schulte, David Rügamer, Thomas Nagler
2025 J jnl
An Interpretable Representation Learning Approach for Diffusion Tensor Imaging.
CoRR
Vishwa Mohan Singh, Alberto Gaston Villagran Asiares, Luisa Sophie Schuhmacher, Kate Rendall, Simon Weißbrod, David Rügamer, Inga Koerte
2025 A* conf
Calibrating LLMs with Information-Theoretic Evidential Deep Learning.
ICLR
Yawei Li, David Rügamer, Bernd Bischl, Mina Rezaei
2025 J jnl
Calibrating LLMs with Information-Theoretic Evidential Deep Learning.
CoRR
Yawei Li, David Rügamer, Bernd Bischl, Mina Rezaei
2025 A* conf
Can Transformers Learn Full Bayesian Inference in Context?
ICML
Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rügamer
2025 J jnl
Can Transformers Learn Full Bayesian Inference in Context?
CoRR
Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rügamer
2025 A* conf
Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries.
ICLR
Chris Kolb, Tobias Weber, Bernd Bischl, David Rügamer
2025 J jnl
Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries.
CoRR
Chris Kolb, Tobias Weber, Bernd Bischl, David Rügamer
2025 J jnl
Differentiable Sparsity via
CoRR
Chris Kolb, Laetitia Frost, Bernd Bischl, David Rügamer
2025 C conf
Enhancing Heavy Rain Nowcasting with Multimodal Data: Integrating Radar and Satellite Observations.
ICMLA
Rama Kassoumeh, David Rügamer, Henning Oppel
2025 J jnl
Enhancing Heavy Rain Nowcasting with Multimodal Data: Integrating Radar and Satellite Observations.
CoRR
Rama Kassoumeh, David Rügamer, Henning Oppel
2025 — conf
Enhancing Traffic Accident Classifications: Application of NLP Methods for City Safety.
ECML/PKDD (10)
Enes Özeren, Alexander Ulbrich, Sascha Filimon, David Rügamer, Andreas Bender
2025 J jnl
Enhancing Traffic Accident Classifications: Application of NLP Methods for City Safety.
CoRR
Enes Özeren, Alexander Ulbrich, Sascha Filimon, David Rügamer, Andreas Bender
2025 J jnl
How To Make Your Cell Tracker Say "I dunno!".
CoRR
Richard D. Paul, Johannes Seiffarth, David Rügamer, Hanno Scharr, Katharina Nöh
2025 A conf
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals.
UAI
Marcel Arpogaus, Thomas Kneib, Thomas Nagler, David Rügamer
2025 J jnl
Hybrid Bernstein Normalizing Flows for Flexible Multivariate Density Regression with Interpretable Marginals.
CoRR
Marcel Arpogaus, Thomas Kneib, Thomas Nagler, David Rügamer
2025 J jnl
Machine learning-based forecasting of daily acute ischemic stroke admissions using weather data.
npj Digit. Medicine
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, Christoph Groden, Michael Neumaier, Fabian Siegel, Máté E. Maros
2025 A* conf
Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks.
ICLR
Emanuel Sommer, Jakob Robnik, Giorgi Nozadze, Uros Seljak, David Rügamer
2025 J jnl
Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks.
CoRR
Emanuel Sommer, Jakob Robnik, Giorgi Nozadze, Uros Seljak, David Rügamer
2025 — conf
On Training Survival Models with Scoring Rules.
ECML/PKDD (7)
Philipp Kopper, David Rügamer, Raphael Sonabend, Bernd Bischl, Andreas Bender
2025 A conf
Paths and Ambient Spaces in Neural Loss Landscapes.
AISTATS
Daniel Dold, Julius Kobialka, Nicolai Palm, Emanuel Sommer, David Rügamer, Oliver Dürr
2025 J jnl
Paths and Ambient Spaces in Neural Loss Landscapes.
CoRR
Daniel Dold, Julius Kobialka, Nicolai Palm, Emanuel Sommer, David Rügamer, Oliver Dürr
2025 — conf
Position: The Future of Bayesian Prediction Is Prior-Fitted.
ICML (Position Papers)
Samuel Müller, Arik Reuter, Noah Hollmann, David Rügamer, Frank Hutter
2025 J jnl
Position: The Future of Bayesian Prediction Is Prior-Fitted.
CoRR
Samuel Müller, Arik Reuter, Noah Hollmann, David Rügamer, Frank Hutter
2025 — conf
Preventing Sensitive Information Leakage via Post-hoc Orthogonalization with Application to Chest Radiograph Embeddings.
PAKDD (4)
Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2025 A* conf
Revisiting Unbiased Implicit Variational Inference.
ICML
Tobias Pielok, Bernd Bischl, David Rügamer
2025 J jnl
Revisiting Unbiased Implicit Variational Inference.
CoRR
Tobias Pielok, Bernd Bischl, David Rügamer
2025 J jnl
Semi-Implicit Variational Inference via Kernelized Path Gradient Descent.
CoRR
Tobias Pielok, Bernd Bischl, David Rügamer
2025 J jnl
Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors.
CoRR
Thomas Nagler, David Rügamer
2024 A* conf
A Functional Extension of Semi-Structured Networks.
NeurIPS
David Rügamer, Bernard X. W. Liew, Zainab Altai, Almond Stöcker
2024 J jnl
A Functional Extension of Semi-Structured Networks.
CoRR
David Rügamer, Bernard X. W. Liew, Zainab Altai, Almond Stöcker
2024 J jnl
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects.
CoRR
David Köhler, David Rügamer, Matthias Schmid
2024 A conf
Bayesian Semi-structured Subspace Inference.
AISTATS
Daniel Dold, David Rügamer, Beate Sick, Oliver Dürr
2024 J jnl
Bayesian Semi-structured Subspace Inference.
CoRR
Daniel Dold, David Rügamer, Beate Sick, Oliver Dürr
2024 A* conf
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
ICML
Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer
2024 J jnl
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
CoRR
Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer
2024 A conf
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction.
WACV
Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2024 J jnl
Estimating Conditional Distributions with Neural Networks Using
J. Stat. Softw.
Lucas Kook, Philipp F. M. Baumann, Oliver Dürr, Beate Sick, David Rügamer
2024 J jnl
Fusing structure from motion and simulation-augmented pose regression from optical flow for challenging indoor environments.
J. Vis. Commun. Image Represent.
Felix Ott, Lucas Heublein, David Rügamer, Bernd Bischl, Christopher Mutschler
2024 A* conf
Generalizing Orthogonalization for Models with Non-Linearities.
ICML
David Rügamer, Chris Kolb, Tobias Weber, Lucas Kook, Thomas Nagler
2024 J jnl
Generalizing Orthogonalization for Models with Non-Linearities.
CoRR
David Rügamer, Chris Kolb, Tobias Weber, Lucas Kook, Thomas Nagler
2024 A conf
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression.
UAI
Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz, Philipp F. M. Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer
2024 J jnl
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression.
CoRR
Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz, Philipp F. M. Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer
2024 J jnl
Interpretable Additive Tabular Transformer Networks.
Trans. Mach. Learn. Res.
Anton Frederik Thielmann, Arik Reuter, Thomas Kneib, David Rügamer, Benjamin Säfken
2024 — conf
Interpretable Machine Learning for TabPFN.
xAI (2)
David Rundel, Julius Kobialka, Constantin von Crailsheim, Matthias Feurer, Thomas Nagler, David Rügamer
2024 J jnl
Interpretable Machine Learning for TabPFN.
CoRR
David Rundel, Julius Kobialka, Constantin von Crailsheim, Matthias Feurer, Thomas Nagler, David Rügamer
2024 J jnl
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.
CoRR
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
2024 A* conf
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.
ICML
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
2024 A* conf
Position: Why We Must Rethink Empirical Research in Machine Learning.
ICML
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
2024 J jnl
Position: Why We Must Rethink Empirical Research in Machine Learning.
CoRR
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
2024 J jnl
Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition.
CoRR
Tobias Weber, Jakob Dexl, David Rügamer, Michael Ingrisch
2024 J jnl
Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models.
Stat. Comput.
Daniel Schalk, Bernd Bischl, David Rügamer
2024 A conf
Scalable Higher-Order Tensor Product Spline Models.
AISTATS
David Rügamer
2024 J jnl
Scalable Higher-Order Tensor Product Spline Models.
CoRR
David Rügamer
2024 — conf
Team MGTD4ADL at SemEval-2024 Task 8: Leveraging (Sentence) Transformer Models with Contrastive Learning for Identifying Machine-Generated Text.
SemEval@NAACL
Huixin Chen, Jan Büssing, David Rügamer, Ercong Nie
2024 A* conf
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract).
IJCAI
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
2024 J jnl
Training Survival Models using Scoring Rules.
CoRR
Philipp Kopper, David Rügamer, Raphael Sonabend, Bernd Bischl, Andreas Bender
2023 A* conf
A New PHO-rmula for Improved Performance of Semi-Structured Networks.
ICML
David Rügamer
2023 J jnl
A New PHO-rmula for Improved Performance of Semi-Structured Networks.
CoRR
David Rügamer
2023 J jnl
Accelerated Componentwise Gradient Boosting Using Efficient Data Representation and Momentum-Based Optimization.
J. Comput. Graph. Stat.
Daniel Schalk, Bernd Bischl, David Rügamer
2023 A* conf
Approximate Bayesian Inference with Stein Functional Variational Gradient Descent.
ICLR
Tobias Pielok, Bernd Bischl, David Rügamer
2023 J jnl
Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition.
IEEE Access
Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2023 J jnl
Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models.
CoRR
Zheyu Zhang, Han Yang, Bolei Ma, David Rügamer, Ercong Nie
2023 — conf
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis.
PAKDD (3)
Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2023 J jnl
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis.
CoRR
Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2023 J jnl
Challenges in Interpreting Epidemiological Surveillance Data - Experiences from Germany.
J. Comput. Graph. Stat.
Cornelius Fritz, Giacomo De Nicola, Felix Günther, David Rügamer, Martje Rave, Marc Schneble, Andreas Bender, Maximilian Weigert, Ralph Brinks, Annika Hoyer, Ursula Berger, Helmut Küchenhoff, Göran Kauermann
2023 J jnl
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction.
CoRR
Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2023 A conf
Frequentist Uncertainty Quantification in Semi-Structured Neural Networks.
AISTATS
Emilio Dorigatti, Benjamin Schubert, Bernd Bischl, David Rügamer
2023 J jnl
Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments.
CoRR
Felix Ott, Lucas Heublein, David Rügamer, Bernd Bischl, Christopher Mutschler
2023 J jnl
Probabilistic time series forecasts with autoregressive transformation models.
Stat. Comput.
David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn
2023 J jnl
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition.
CoRR
Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2023 J jnl
Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization.
CoRR
Chris Kolb, Christian L. Müller, Bernd Bischl, David Rügamer
2023 — conf
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry.
ECML/PKDD (1)
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
2023 J jnl
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry.
CoRR
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
2023 J jnl
Unreading Race: Purging Protected Features from Chest X-ray Embeddings.
CoRR
Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2023 J jnl
deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression.
J. Stat. Softw.
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
2022 J jnl
ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling.
CoRR
Philipp Schiele, Christoph Berninger, David Rügamer
2022 J jnl
Additive Higher-Order Factorization Machines.
CoRR
David Rügamer
2022 J jnl
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens.
CoRR
Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler
2022 J jnl
Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression.
CoRR
Felix Ott, Nisha Lakshmana Raichur, David Rügamer, Tobias Feigl, Heiko Neumann, Bernd Bischl, Christopher Mutschler
2022 J jnl
Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens.
Int. J. Document Anal. Recognit.
Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler
2022 J jnl
Cross-Modal Common Representation Learning with Triplet Loss Functions.
CoRR
Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2022 — conf
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis.
PAKDD (2)
Philipp Kopper, Simon Wiegrebe, Bernd Bischl, Andreas Bender, David Rügamer
2022 J jnl
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis.
CoRR
Philipp Kopper, Simon Wiegrebe, Bernd Bischl, Andreas Bender, David Rügamer
2022 — conf
Developing Open Source Educational Resources for Machine Learning and Data Science.
Teaching ML
Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl
2022 A* conf
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift.
ACM Multimedia
Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2022 J jnl
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift.
CoRR
Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2022 — conf
Factorized Structured Regression for Large-Scale Varying Coefficient Models.
ECML/PKDD (5)
David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl
2022 J jnl
Factorized Structured Regression for Large-Scale Varying Coefficient Models.
CoRR
David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl
2022 — conf
Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs.
MAD@MICCAI
Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2022 A conf
Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach.
WACV
Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2022 — conf
Joint Debiased Representation Learning and Imbalanced Data Clustering.
ICDM (Workshops)
Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl
2022 J jnl
Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models.
CoRR
Daniel Schalk, Bernd Bischl, David Rügamer
2022 — conf
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition.
ICPR Workshops (1)
Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
2022 J jnl
Selective inference for additive and linear mixed models.
Comput. Stat. Data Anal.
David Rügamer, Philipp F. M. Baumann, Sonja Greven
2022 J jnl
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift.
CoRR
Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott
2022 — conf
Uncertainty-aware Evaluation of Time-series Classification for Online Handwriting Recognition with Domain Shift.
STRL@IJCAI
Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott
2022 J jnl
Uncertainty-aware predictive modeling for fair data-driven decisions.
CoRR
Patrick Kaiser, Christoph Kern, David Rügamer
2021 J jnl
Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization.
CoRR
Daniel Schalk, Bernd Bischl, David Rügamer
2021 J jnl
Automatic Componentwise Boosting: An Interpretable AutoML System.
CoRR
Stefan Coors, Daniel Schalk, Bernd Bischl, David Rügamer
2021 J jnl
Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany.
CoRR
Cornelius Fritz, Emilio Dorigatti, David Rügamer
2021 J jnl
Conditional Model Selection in Mixed-Effects Models with cAIC4.
J. Stat. Softw.
Benjamin Säfken, David Rügamer, Thomas Kneib, Sonja Greven
2021 — conf
Deep Conditional Transformation Models.
ECML/PKDD (3)
Philipp F. M. Baumann, Torsten Hothorn, David Rügamer
2021 J jnl
Deep Semi-Supervised Learning for Time Series Classification.
CoRR
Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl
2021 C conf
Deep Semi-supervised Learning for Time Series Classification.
ICMLA
Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl
2021 J jnl
Developing Open Source Educational Resources for Machine Learning and Data Science.
CoRR
Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl
2021 J jnl
Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach.
CoRR
Magdalena Mittermeier, Maximilian Weigert, David Rügamer
2021 J jnl
Learning Statistical Representation with Joint Deep Embedded Clustering.
CoRR
Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl
2021 — conf
Semi-Structured Deep Piecewise Exponential Models.
SPACA
Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer
2021 J jnl
Survival-oriented embeddings for improving accessibility to complex data structures.
CoRR
Tobias Weber, Michael Ingrisch, Matthias Fabritius, Bernd Bischl, David Rügamer
2021 J jnl
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation.
CoRR
Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer
2021 J jnl
Transforming Autoregression: Interpretable and Expressive Time Series Forecast.
CoRR
David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn
2021 J jnl
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression.
CoRR
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
2020 — conf
A General Machine Learning Framework for Survival Analysis.
ECML/PKDD (3)
Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl
2020 J jnl
A General Machine Learning Framework for Survival Analysis.
CoRR
Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl
2020 J jnl
A Unifying Network Architecture for Semi-Structured Deep Distributional Learning.
CoRR
David Rügamer, Chris Kolb, Nadja Klein
2020 J jnl
Deep Conditional Transformation Models.
CoRR
Philipp F. M. Baumann, Torsten Hothorn, David Rügamer
2020 J jnl
Inference for L
Stat. Comput.
David Rügamer, Sonja Greven
2020 J jnl
Neural Mixture Distributional Regression.
CoRR
David Rügamer, Florian Pfisterer, Bernd Bischl
2020 J jnl
Semi-Structured Deep Piecewise Exponential Models.
CoRR
Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer
2018 J jnl
Valid Inference for L
CoRR
David Rügamer, Sonja Greven