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2020 – today
- 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) - [c12]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. PAKDD (3) 2023: 180-191 - [i32]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) - [i31]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis. CoRR abs/2303.11224 (2023) - [i30]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) - [i29]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) - 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) - [c11]Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl:
Joint Debiased Representation Learning and Imbalanced Data Clustering. ICDM (Workshops) 2022: 55-62 - [c10]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 - [c9]Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer:
Implicit Embeddings via GAN Inversion for High Resolution Chest Radiographs. MAD@MICCAI 2022: 22-32 - [c8]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 - [c7]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 - [c6]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 - [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
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last updated on 2023-06-05 01:56 CEST by the dblp team
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