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2020 – today
- 2024
[j12]Felix Ott
, Lucas Heublein, David Rügamer, Bernd Bischl, Christopher Mutschler:
Fusing structure from motion and simulation-augmented pose regression from optical flow for challenging indoor environments. J. Vis. Commun. Image Represent. 103: 104256 (2024)- [j11]Daniel Schalk, Bernd Bischl, David Rügamer:
Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models. Stat. Comput. 34(1): 31 (2024) - [j10]Anton Frederik Thielmann, Arik Reuter, Thomas Kneib, David Rügamer, Benjamin Säfken:
Interpretable Additive Tabular Transformer Networks. Trans. Mach. Learn. Res. 2024 (2024) - [c28]David Rügamer:
Scalable Higher-Order Tensor Product Spline Models. AISTATS 2024: 1-9 - [c27]Daniel Dold, David Rügamer, Beate Sick, Oliver Dürr:
Bayesian Semi-structured Subspace Inference. AISTATS 2024: 1819-1827 - [c26]Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl:
Position: Why We Must Rethink Empirical Research in Machine Learning. ICML 2024 - [c25]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:
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [c24]David Rügamer, Chris Kolb, Tobias Weber, Lucas Kook, Thomas Nagler:
Generalizing Orthogonalization for Models with Non-Linearities. ICML 2024 - [c23]Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer:
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? ICML 2024 - [c22]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract). IJCAI 2024: 8466-8470 - [c21]Huixin Chen, Jan Büssing, David Rügamer, Ercong Nie:
Team MGTD4ADL at SemEval-2024 Task 8: Leveraging (Sentence) Transformer Models with Contrastive Learning for Identifying Machine-Generated Text. SemEval@NAACL 2024: 1711-1718 - [c20]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction. WACV 2024: 7650-7659 - [c19]David Rundel, Julius Kobialka, Constantin von Crailsheim, Matthias Feurer, Thomas Nagler, David Rügamer:
Interpretable Machine Learning for TabPFN. xAI (2) 2024: 465-476 - [i48]Daniel Dold, David Rügamer, Beate Sick, Oliver Dürr:
Bayesian Semi-structured Subspace Inference. CoRR abs/2401.12950 (2024) - [i47]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:
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR abs/2402.00809 (2024) - [i46]David Rügamer:
Scalable Higher-Order Tensor Product Spline Models. CoRR abs/2402.01090 (2024) - [i45]Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer:
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? CoRR abs/2402.01484 (2024) - [i44]David Rundel, Julius Kobialka, Constantin von Crailsheim, Matthias Feurer, Thomas Nagler, David Rügamer:
Interpretable Machine Learning for TabPFN. CoRR abs/2403.10923 (2024) - [i43]Philipp Kopper, David Rügamer, Raphael Sonabend, Bernd Bischl, Andreas Bender:
Training Survival Models using Scoring Rules. CoRR abs/2403.13150 (2024) - [i42]Tobias Weber, Jakob Dexl, David Rügamer, Michael Ingrisch:
Post-Training Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition. CoRR abs/2404.09683 (2024) - [i41]Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl:
Position: Why We Must Rethink Empirical Research in Machine Learning. CoRR abs/2405.02200 (2024) - [i40]David Rügamer, Chris Kolb, Tobias Weber, Lucas Kook, Thomas Nagler:
Generalizing Orthogonalization for Models with Non-Linearities. CoRR abs/2405.02475 (2024) - [i39]Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz, Philipp F. M. Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer:
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression. CoRR abs/2405.05429 (2024) - [i38]David Köhler, David Rügamer, Matthias Schmid:
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects. CoRR abs/2407.18650 (2024) - 2023
- [j9]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition. IEEE Access 11: 94148-94172 (2023) - [j8]Daniel Schalk, Bernd Bischl, David Rügamer:
Accelerated Componentwise Gradient Boosting Using Efficient Data Representation and Momentum-Based Optimization. J. Comput. Graph. Stat. 32(2): 631-641 (2023) - [j7]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:
Challenges in Interpreting Epidemiological Surveillance Data - Experiences from Germany. J. Comput. Graph. Stat. 32(3): 765-766 (2023) - [j6]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:
deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. J. Stat. Softw. 105(2) (2023) - [j5]David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn:
Probabilistic time series forecasts with autoregressive transformation models. Stat. Comput. 33(2): 37 (2023) - [c18]Emilio Dorigatti, Benjamin Schubert, Bernd Bischl, David Rügamer:
Frequentist Uncertainty Quantification in Semi-Structured Neural Networks. AISTATS 2023: 1924-1941 - [c17]Tobias Pielok, Bernd Bischl, David Rügamer:
Approximate Bayesian Inference with Stein Functional Variational Gradient Descent. ICLR 2023 - [c16]David Rügamer:
A New PHO-rmula for Improved Performance of Semi-Structured Networks. ICML 2023: 29291-29305 - [c15]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. PAKDD (3) 2023: 180-191 - [c14]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. ECML/PKDD (1) 2023: 459-474 - [i37]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition. CoRR abs/2301.06293 (2023) - [i36]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. CoRR abs/2303.11224 (2023) - [i35]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. CoRR abs/2304.02902 (2023) - [i34]Felix Ott, Lucas Heublein, David Rügamer, Bernd Bischl, Christopher Mutschler:
Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments. CoRR abs/2304.07250 (2023) - [i33]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction. CoRR abs/2305.16376 (2023) - [i32]David Rügamer:
A New PHO-rmula for Improved Performance of Semi-Structured Networks. CoRR abs/2306.00522 (2023) - [i31]Chris Kolb, Christian L. Müller, Bernd Bischl, David Rügamer:
Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization. CoRR abs/2307.03571 (2023) - [i30]Zheyu Zhang, Han Yang, Bolei Ma, David Rügamer, Ercong Nie:
Baby's CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models. CoRR abs/2308.01684 (2023) - [i29]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Unreading Race: Purging Protected Features from Chest X-ray Embeddings. CoRR abs/2311.01349 (2023) - 2022
- [j4]David Rügamer, Philipp F. M. Baumann, Sonja Greven:
Selective inference for additive and linear mixed models. Comput. Stat. Data Anal. 167: 107350 (2022) - [j3]Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler:
Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens. Int. J. Document Anal. Recognit. 25(4): 385-414 (2022) - [c13]Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl:
Joint Debiased Representation Learning and Imbalanced Data Clustering. ICDM (Workshops) 2022: 55-62 - [c12]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition. ICPR Workshops (1) 2022: 373-383 - [c11]Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott:
Uncertainty-aware Evaluation of Time-series Classification for Online Handwriting Recognition with Domain Shift. STRL@IJCAI 2022 - [c10]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs. MAD@MICCAI 2022: 22-32 - [c9]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. ACM Multimedia 2022: 5934-5943 - [c8]Philipp Kopper, Simon Wiegrebe, Bernd Bischl, Andreas Bender, David Rügamer:
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis. PAKDD (2) 2022: 249-261 - [c7]David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl:
Factorized Structured Regression for Large-Scale Varying Coefficient Models. ECML/PKDD (5) 2022: 20-35 - [c6]Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl:
Developing Open Source Educational Resources for Machine Learning and Data Science. Teaching ML 2022: 1-6 - [c5]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach. WACV 2022: 1244-1254 - [i28]Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler:
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens. CoRR abs/2202.07036 (2022) - [i27]Philipp Kopper, Simon Wiegrebe, Bernd Bischl, Andreas Bender, David Rügamer:
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis. CoRR abs/2202.07423 (2022) - [i26]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Cross-Modal Common Representation Learning with Triplet Loss Functions. CoRR abs/2202.07901 (2022) - [i25]Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler:
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift. CoRR abs/2204.03342 (2022) - [i24]David Rügamer, Andreas Bender, Simon Wiegrebe, Daniel Racek, Bernd Bischl, Christian L. Müller, Clemens Stachl:
Factorized Structured Regression for Large-Scale Varying Coefficient Models. CoRR abs/2205.13080 (2022) - [i23]David Rügamer:
Additive Higher-Order Factorization Machines. CoRR abs/2205.14515 (2022) - [i22]Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott:
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift. CoRR abs/2206.08640 (2022) - [i21]Felix Ott, Nisha Lakshmana Raichur, David Rügamer, Tobias Feigl, Heiko Neumann, Bernd Bischl, Christopher Mutschler:
Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression. CoRR abs/2208.00919 (2022) - [i20]Philipp Schiele, Christoph Berninger, David Rügamer:
ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling. CoRR abs/2208.14919 (2022) - [i19]Daniel Schalk, Bernd Bischl, David Rügamer:
Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models. CoRR abs/2210.07723 (2022) - [i18]Patrick Kaiser, Christoph Kern, David Rügamer:
Uncertainty-aware predictive modeling for fair data-driven decisions. CoRR abs/2211.02730 (2022) - 2021
- [j2]Benjamin Säfken, David Rügamer, Thomas Kneib, Sonja Greven:
Conditional Model Selection in Mixed-Effects Models with cAIC4. J. Stat. Softw. 99(8) (2021) - [c4]Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl:
Deep Semi-supervised Learning for Time Series Classification. ICMLA 2021: 422-428 - [c3]Philipp F. M. Baumann, Torsten Hothorn, David Rügamer:
Deep Conditional Transformation Models. ECML/PKDD (3) 2021: 3-18 - [c2]Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer:
Semi-Structured Deep Piecewise Exponential Models. SPACA 2021: 40-53 - [i17]Cornelius Fritz, Emilio Dorigatti, David Rügamer:
Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. CoRR abs/2101.00661 (2021) - [i16]Jann Goschenhofer, Rasmus Hvingelby, David Rügamer, Janek Thomas, Moritz Wagner, Bernd Bischl:
Deep Semi-Supervised Learning for Time Series Classification. CoRR abs/2102.03622 (2021) - [i15]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:
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. CoRR abs/2104.02705 (2021) - [i14]Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl:
Developing Open Source Educational Resources for Machine Learning and Data Science. CoRR abs/2107.14330 (2021) - [i13]Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl:
Learning Statistical Representation with Joint Deep Embedded Clustering. CoRR abs/2109.05232 (2021) - [i12]Stefan Coors, Daniel Schalk, Bernd Bischl, David Rügamer:
Automatic Componentwise Boosting: An Interpretable AutoML System. CoRR abs/2109.05583 (2021) - [i11]Daniel Schalk, Bernd Bischl, David Rügamer:
Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization. CoRR abs/2110.03513 (2021) - [i10]David Rügamer, Philipp F. M. Baumann, Thomas Kneib, Torsten Hothorn:
Transforming Autoregression: Interpretable and Expressive Time Series Forecast. CoRR abs/2110.08248 (2021) - [i9]Tobias Weber, Michael Ingrisch, Matthias Fabritius, Bernd Bischl, David Rügamer:
Survival-oriented embeddings for improving accessibility to complex data structures. CoRR abs/2110.11303 (2021) - [i8]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. CoRR abs/2110.11312 (2021) - [i7]Magdalena Mittermeier, Maximilian Weigert, David Rügamer:
Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach. CoRR abs/2111.05303 (2021) - 2020
- [j1]David Rügamer, Sonja Greven:
Inference for L2-Boosting. Stat. Comput. 30(2): 279-289 (2020) - [c1]Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl:
A General Machine Learning Framework for Survival Analysis. ECML/PKDD (3) 2020: 158-173 - [i6]David Rügamer, Chris Kolb, Nadja Klein:
A Unifying Network Architecture for Semi-Structured Deep Distributional Learning. CoRR abs/2002.05777 (2020) - [i5]Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl:
A General Machine Learning Framework for Survival Analysis. CoRR abs/2006.15442 (2020) - [i4]David Rügamer, Florian Pfisterer, Bernd Bischl:
Neural Mixture Distributional Regression. CoRR abs/2010.06889 (2020) - [i3]Philipp F. M. Baumann, Torsten Hothorn, David Rügamer:
Deep Conditional Transformation Models. CoRR abs/2010.07860 (2020) - [i2]Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer:
Semi-Structured Deep Piecewise Exponential Models. CoRR abs/2011.05824 (2020)
2010 – 2019
- 2018
- [i1]David Rügamer, Sonja Greven:
Valid Inference for L2-Boosting. CoRR abs/1805.01852 (2018)
Coauthor Index
[j12] [j11] [c26] [c23] [c22] [c20] [i45] [i43] [i41] [j9] [j8] [j6] [c18] [c17] [c15] [c14] [i37] [i36] [i35] [i34] [i33] [i31] [i29] [j3] [c13] [c12] [c11] [c10] [c9] [c8] [c7] [c6] [c5] [i28] [i27] [i26] [i25] [i24] [i22] [i21] [i19] [c4] [c2] [i16] [i15] [i14] [i13] [i12] [i11] [i9] [i8] [c1] [i5] [i4] [i2]
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