Probabilistic Machine and Deep Learning

This group focuses on methodological and applied research in the context of probabilistc machine and deep learning. Topics include

Members

Name       Position
Prof. Dr. David Rügamer       Lead
Dr. Andreas Bender       PostDoc
Dr. Ludwig Bothmann       PostDoc
Dr. Mina Rezaei       PostDoc
Martin Binder       PhD Student
Lukas Burk       PhD Student
Daniel Dold       PhD Student
Emilio Dorigatti       PhD Student
Jann Goschenhofer       PhD Student
Florian Karl       PhD Student
Chris Kolb       PhD Student
Philipp Kopper       PhD Student
Felix Ott       PhD Student
Tobias Pielok       PhD Student
Katharina Rath       PhD Student
Theresa Stüber       PhD Student
Tobias Weber       PhD Student
Lisa Wimmer       PhD Student

Publications

  1. Rundel D, Kobialka J, Crailsheim C von, Feurer M, Nagler T, Rügamer D (2024) Interpretable Machine Learning for TabPFN 2nd World Conference on eXplainable Artificial Intelligence,
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  2. Kook L, Baumann PFM, Dürr O, Sick B, Rügamer D (2024) Estimating Conditional Distributions with Neural Networks using R package deeptrafo. Journal of Statistical Software.
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  3. Kopper P, Rügamer D, Sonabend R, Bischl B, Bender A (2024) Training Survival Models using Scoring Rules.
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  4. Sommer E, Wimmer L, Papamarkou T, Bothmann L, Bischl B, Rügamer D (2024) Connecting the Dots: Is Mode Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
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  5. Papamarkou T, Skoularidou M, Palla K, Aitchison L, Arbel J, Dunson D, Filippone M, Fortuin V, Hennig P, Hubin A, Immer A, Karaletsos T, Khan ME, Kristiadi A, Li Y, Lobato JMH, Mandt S, Nemeth C, Osborne MA, Rudner TGJ, Rügamer D, Teh YW, Welling M, Wilson AG, Zhang R (2024) Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.
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  6. Rügamer D (2024) Scalable Higher-Order Tensor Product Spline Models Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR.
  7. Dold D, Rügamer D, Sick B, Dürr O (2024) Semi-Structured Subspace Inference Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR.
  8. Schalk D, Bischl B, Rügamer D (2024) Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models. Statistics & Computing 34.
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  9. Weber T, Ingrisch M, Bischl B, Rügamer D (2024) Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstruction Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV),
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  10. Weber T, Ingrisch M, Bischl B, Rügamer D (2023) Unreading Race: Purging Protected Features from Chest X-ray Embeddings. arXiv:2311.01349.
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  11. Rügamer D, Pfisterer F, Bischl B, Grün B (2023) Mixture of Experts Distributional Regression: Implementation Using Robust Estimation with Adaptive First-order Methods. AStA Advances in Statistical Analysis.
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  12. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2023) Auxiliary Cross-Modal Representation Learning With Triplet Loss Functions for Online Handwriting Recognition. IEEE Access 11, 94148–94172.
  13. Kolb C, Müller CL, Bischl B, Rügamer D (2023) Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization. arXiv preprint arXiv:2307.03571.
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  14. Kolb C, Bischl B, Müller CL, Rügamer D (2023) Sparse Modality Regression Proceedings of the 37th International Workshop on Statistical Modelling, IWSM 2023,
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  15. Wiese JG, Wimmer L, Papamarkou T, Bischl B, Günnemann S, Rügamer D (2023) Towards Efficient Posterior Sampling in Deep Neural Networks via Symmetry Removal Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer International Publishing.
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  16. Rügamer D (2023) A New PHO-rmula for Improved Performance of Semi-Structured Networks. ICML 2023.
  17. Rath K, Rügamer D, Bischl B, Toussaint U von, Albert C (2023) Dependent state space Student-t processes for imputation and data augmentation in plasma diagnostics. Contributions to Plasma Physics.
  18. Weber T, Ingrisch M, Bischl B, Rügamer D (2023) Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis Advances in Knowledge Discovery and Data Mining: 27th Pacific-Asia Conference, PAKDD 2023,
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  19. Ott F, Raichur NL, Rügamer D, Feigl T, Neumann H, Bischl B, Mutschler C (2023) Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression. arXiv:2208.00919.
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  20. Dorigatti E, Bischl B, Rügamer D (2023) Frequentist Uncertainty Quantification in Semi-Structured Neural Networks International Conference on Artificial Intelligence and Statistics, PMLR.
  21. Pielok T, Bischl B, Rügamer D (2023) Approximate Bayesian Inference with Stein Functional Variational Gradient Descent International Conference on Learning Representations,
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  22. Wimmer L, Sale Y, Hofman P, Bischl B, Hüllermeier E (2023) Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023),
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  23. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Representation Learning for Tablet and Paper Domain Adaptation in favor of Online Handwriting Recognition MPRSS 2022,
  24. Ziegler I, Ma B, Nie E, Bischl B, Rügamer D, Schubert B, Dorigatti E (2022) What cleaves? Is proteasomal cleavage prediction reaching a ceiling? NeurIPS 2022 Workshop on Learning Meaningful Representations of Life (LMRL),
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  25. Kaiser P, Rügamer D, Kern C (2022) Uncertainty as a key to fair data-driven decision making NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning (TSRML),
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  26. Ott F, Rügamer D, Heublein L, Hamann T, Barth J, Bischl B, Mutschler C (2022) Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens. International Journal on Document Analysis and Recognition (IJDAR).
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  27. Schiele P, Berninger C, Rügamer D (2022) ARMA Cell: A Modular and Effective Approach for Neural Autoregressive Modeling. arXiv preprint arXiv:2208.14919.
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  28. Rath K, Rügamer D, Bischl B, Toussaint U von, Rea C, Maris A, Granetz R, Albert C (2022) Data augmentation for disruption prediction via robust surrogate models. Journal of Plasma Physics.
  29. Mittermeier M, Weigert M, Rügamer D, Küchenhoff H, Ludwig R (2022) A Deep Learning Version of Hess & Brezowskys Classification of Großwetterlagen over Europe: Projection of Future Changes in a CMIP6 Large Ensemble. Environmental Research Letters.
  30. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift ACM Multimedia,
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  31. Rügamer D, Bender A, Wiegrebe S, Racek D, Bischl B, Müller C, Stachl C (2022) Factorized Structured Regression for Large-Scale Varying Coefficient Models Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), Springer International Publishing.
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  32. Klaß A, Lorenz S, Lauer-Schmaltz M, Rügamer D, Bischl B, Mutschler C, Ott F (2022) Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift IJCAI-ECAI 2022, 1st International Workshop on Spatio-Temporal Reasoning and Learning,
  33. Fritz C, Nicola GD, Günther F, Rügamer D, Rave M, Schneble M, Bender A, Weigert M, Brinks R, Hoyer A, Berger U, Küchenhoff H, Kauermann G (2022) Challenges in Interpreting Epidemiological Surveillance Data - Experiences from Germany. Journal of Computational & Graphical Statistics.
  34. Rügamer D (2022) Additive Higher-Order Factorization Machines. arXiv preprint arXiv:2205.14515.
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  35. Rügamer D, Kolb C, Fritz C, Pfisterer F, Kopper P, Bischl B, Shen R, Bukas C, Sousa LB de Andrade e, Thalmeier D, Baumann P, Kook L, Klein N, Müller CL (2022) deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. Journal of Statistical Software (provisionally accepted).
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  36. Fritz C, Dorigatti E, Rügamer D (2022) Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. Scientific Reports 12, 2045–2322.
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  37. Rügamer D, Baumann P, Greven S (2022) Selective Inference for Additive and Mixed Models. Computational Statistics and Data Analysis 167, 107350.
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  38. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2022) Cross-Modal Common Representation Learning with Triplet Loss Functions. arXiv preprint arXiv:2202.07901.
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  39. Dorigatti E, Goschenhofer J, Schubert B, Rezaei M, Bischl B (2022) Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection. arXiv preprint arXiv:2109.05232.
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  40. Kopper P, Wiegrebe S, Bischl B, Bender A, Rügamer D (2022) DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis Advances in Knowledge Discovery and Data Mining, pp. 249–261. Springer International Publishing.
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  41. Liew BXW, Rügamer D, Duffy K, Taylor M, Jackson J (2021) The mechanical energetics of walking across the adult lifespan. PloS one 16, e0259817.
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  42. Mittermeier M, Weigert M, Rügamer D (2021) Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach. NeurIPS 2021, Tackling Climate Change with Machine Learning.
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  43. Weber T, Ingrisch M, Bischl B, Rügamer D (2021) Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. NeurIPS 2021 Workshops, Deep Generative Models and Downstream Applications.
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  44. Weber T, Ingrisch M, Fabritius M, Bischl B, Rügamer D (2021) Survival-oriented embeddings for improving accessibility to complex data structures. NeurIPS 2021 Workshops, Bridging the Gap: From Machine Learning Research to Clinical Practice.
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  45. Liew BXW, Rügamer D, Zhai XJ, Morris S, Netto K (2021) Comparing machine, deep, and transfer learning in predicting joint moments in running. Journal of Biomechanics.
  46. Ott F, Rügamer D, Heublein L, Bischl B, Mutschler C (2021) Joint Classification and Trajectory Regression of Online Handwriting using a Multi-Task Learning Approach Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV),
  47. Goschenhofer J, Hvingelby R, Rügamer D, Thomas J, Wagner M, Bischl B (2021) Deep Semi-Supervised Learning for Time Series Classification 20th IEEE International Conference on Machine Learning and Applications (ICMLA),
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  48. Falla D, Devecchi V, Jimenez-Grande D, Rügamer D, Liew B (2021) Modern Machine Learning Approaches Applied in Spinal Pain Research. Journal of Electromyography and Kinesiology.
  49. Berninger C, Stöcker A, Rügamer D (2021) A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction. Journal of Forecasting.
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  50. Baumann PFM, Hothorn T, Rügamer D (2021) Deep Conditional Transformation Models Machine Learning and Knowledge Discovery in Databases. Research Track, pp. 3–18. Springer International Publishing.
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  51. Rath K, Albert CG, Bischl B, Toussaint U von (2021) Symplectic Gaussian process regression of maps in Hamiltonian systems. Chaos: An Interdisciplinary Journal of Nonlinear Science 31, 053121.
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  52. Bender A, Rügamer D, Scheipl F, Bischl B (2021) A General Machine Learning Framework for Survival Analysis. In: In: Hutter F , In: Kersting K , In: Lijffijt J , In: Valera I (eds) Machine Learning and Knowledge Discovery in Databases, pp. 158–173. Springer International Publishing.
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  53. Kopper P, Pölsterl S, Wachinger C, Bischl B, Bender A, Rügamer D (2021) Semi-Structured Deep Piecewise Exponential Models. In: In: Greiner R , In: Kumar N , In: Gerds TA , In: Schaar M van der (eds) Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, pp. 40–53. PMLR.
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  54. Günther F, Bender A, Katz K, Küchenhoff H, Höhle M (2020) Nowcasting the COVID-19 pandemic in Bavaria. Biometrical Journal.
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  55. Liew BXW, Peolsson A, Rügamer D, Wibault J, Löfgren H, Dedering A, Zsigmond P, Falla D (2020) Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy – a machine learning approach. Scientific Reports.
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  56. Rügamer D, Pfisterer F, Bischl B (2020) Neural Mixture Distributional Regression. arXiv:2010.06889 [cs, stat].
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  57. Bender A, Python A, Lindsay SW, Golding N, Moyes CL (2020) Modelling geospatial distributions of the triatomine vectors of Trypanosoma cruzi in Latin America. PLOS Neglected Tropical Diseases 14, e0008411.
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  58. Beggel L, Pfeiffer M, Bischl B (2020) Robust Anomaly Detection in Images using Adversarial Autoencoders Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 206–222. Springer.
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  59. Pfister FMJ, Um TT, Pichler DC, Goschenhofer J, Abedinpour K, Lang M, Endo S, Ceballos-Baumann AO, Hirche S, Bischl B, others (2020) High-Resolution Motor State Detection in parkinson’s Disease Using convolutional neural networks. Scientific reports 10, 1–11.
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  60. Rügamer D, Greven S (2020) Inference for L2-Boosting. Statistics and Computing 30, 279–289.
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  61. Stachl C, Au Q, Schoedel R, Gosling SD, Harari GM, Buschek D, Völkel ST, Schuwerk T, Oldemeier M, Ullmann T, others (2020) Predicting personality from patterns of behavior collected with smartphones. Proceedings of the National Academy of Sciences.
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  62. Sun X, Bischl B (2019) Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning 2019 IEEE Symposium Series on Computational Intelligence (SSCI),
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  63. Pfisterer F, Beggel L, Sun X, Scheipl F, Bischl B (2019) Benchmarking time series classification – Functional data vs machine learning approaches. arXiv preprint arXiv:1911.07511.
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  64. Beggel L, Kausler BX, Schiegg M, Pfeiffer M, Bischl B (2019) Time series anomaly detection based on shapelet learning. Computational Statistics 34, 945–976.
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  65. Goschenhofer J, Pfister FMJ, Yuksel KA, Bischl B, Fietzek U, Thomas J (2019) Wearable-based Parkinson’s Disease Severity Monitoring using Deep Learning (U Brefeld, E Fromont, A Hotho, A Knobbe, M Maathuis, and C Robardet, Eds.). Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, 400–415.
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  66. Sun X, Gossmann A, Wang Y, Bischt B (2019) Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1344–1353.
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  67. Sun X, Wang Y, Gossmann A, Bischl B (2019) Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks. CoRR.
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  68. Rügamer D, Greven S (2018) Selective inference after likelihood-or test-based model selection in linear models. Statistics & Probability Letters 140, 7–12.
  69. Rügamer D, Kolb C, Klein N (2023) Semi-Structured Distributional Regression. The American Statistician.
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  70. Rueger S, Goschenhofer J, Nath A, Firsching M, Ennen A, Bischl B (2022) Deep-Learning-based Aluminum Sorting on Dual Energy X-Ray Transmission Data. Sensor-based Sorting and Control.
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Contact

Feel free to contact us if you are looking for collaborations:

david.ruegamer [at] stat.uni-muenchen.de