Rügamer, David

39 publications

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

ICML 2025 Adjustment for Confounding Using Pre-Trained Representations Rickmer Schulte, David Rügamer, Thomas Nagler

ICLRW 2025 Adjustment for Confounding Using Pre-Trained Representations Rickmer Schulte, David Rügamer, Thomas Nagler

ICLRW 2025 Approximate Posteriors in Neural Networks: A Sampling Perspective Julius Kobialka, Emanuel Sommer, Juntae Kwon, Daniel Dold, David Rügamer

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

ICML 2025 Can Transformers Learn Full Bayesian Inference in Context? Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rügamer

ICLRW 2025 Can Transformers Learn Full Bayesian Inference in Context? Arik Reuter, Tim G. J. Rudner, Vincent Fortuin, David Rügamer

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

ICLRW 2025 Differentiable Attention Sparsity via Structured $d$-Gating Chris Kolb, Bernd Bischl, David Rügamer

NeurIPS 2025 Differentiable Sparsity via $d$-Gating: Simple and Versatile Structured Penalization Chris Kolb, Laetitia Frost, Bernd Bischl, David Rügamer

ICLRW 2025 Efficiently Warmstarting MCMC for BNNs David Rundel, Emanuel Sommer, Bernd Bischl, David Rügamer, Matthias Feurer

ECML-PKDD 2025 Enhancing Traffic Accident Classifications: Application of NLP Methods for City Safety Enes Özeren, Alexander Ulbrich, Sascha Filimon, David Rügamer, Andreas Bender

ICCV 2025 How to Make Your Cell Tracker Say "i Dunno!" Richard D. Paul, Johannes Seiffarth, David Rügamer, Katharina Nöh, Hanno Scharr

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

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

ECML-PKDD 2025 On Training Survival Models with Scoring Rules Philipp Kopper, David Rügamer, Raphael Sonabend, Bernd Bischl, Andreas Bender

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

ICML 2025 Position: The Future of Bayesian Prediction Is Prior-Fitted Samuel Müller, Arik Reuter, Noah Hollmann, David Rügamer, Frank Hutter

ICML 2025 Revisiting Unbiased Implicit Variational Inference Tobias Pielok, Bernd Bischl, David Rügamer

ICLRW 2025 Uncertainty Quantification for Prior-Fitted Networks Using Martingale Posteriors Thomas Nagler, David Rügamer

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

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

WACV 2024 Constrained Probabilistic Mask Learning for Task-Specific Undersampled MRI Reconstruction Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer

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

UAI 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 Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer

TMLR 2024 Interpretable Additive Tabular Transformer Networks Anton Frederik Thielmann, Arik Reuter, Thomas Kneib, David Rügamer, Benjamin Säfken

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 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

ICML 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

IJCAI 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

ICML 2023 A New PHO-Rmula for Improved Performance of Semi-Structured Networks David Rügamer

ICLR 2023 Approximate Bayesian Inference with Stein Functional Variational Gradient Descent Tobias Pielok, Bernd Bischl, David Rügamer

ECML-PKDD 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 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

WACV 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

NeurIPSW 2022 Uncertainty-Aware Predictive Modeling for Fair Data-Driven Decisions Patrick Kaiser, Christoph Kern, David Rügamer

NeurIPSW 2022 What Cleaves? Is Proteasomal Cleavage Prediction Reaching a Ceiling? Ingo Ziegler, Bolei Ma, Ercong Nie, Bernd Bischl, David Rügamer, Benjamin Schubert, Emilio Dorigatti

ECML-PKDD 2021 Deep Conditional Transformation Models Philipp F. M. Baumann, Torsten Hothorn, David Rügamer

NeurIPSW 2021 Towards Modelling Hazard Factors in Unstructured Data Spaces Using Gradient-Based Latent Interpolation Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer

ECML-PKDD 2020 A General Machine Learning Framework for Survival Analysis Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl