David Rügamer

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

David.Ruegamer [at] stat.uni-muenchen.de

Teaching

• Summer 2021:
  • Applied Deep Learning with TensorFlow and PyTorch
  • Predictive Modeling
• Winter 2020/21:
  • Deep Learning
• Summer 2020:
  • Introduction to R
  • Predictive Modeling

Thesis

I offer various topics for Master theses mainly but not exclusively on the topics listed below. If you are interested, please send me your field of interest or your ideas, a CV and your current transcript of records.

Research Interests

My main research interest lies in the combination of structured additive models and deep neural networks. In deep learning, combinations of additive models and deep neural networks are sometimes referred to as wide and deep learning, although there is no formal definition and the types of networks do not necessarily focus on structured models for the wide part. Semi-structured deep learning is my very own interpretation that has several important properties (read the paper :)). The broader topic I am interested in is Uncertainty quantification in deep learning. Other topics I work on / find interesting:

• Selective Inference
• Gradient Boosting
• Distributional Regression
• Transformation Models / Normalizing Flows
• Functional Data Analysis
• Time Series Analysis
• Survival Analysis
• Recommender Systems, Matrix and Tensor Factorization
• Optimization

News

News on my personal homepage

You Can Find me on

• GitHub
• ResearchGate
• Google Scholar

Software

• deepregression Semi-Structured Deep Distributional Regression on Github and CRAN
• selfmade SELective inference For Mixed and ADditive model Estimators on Github and CRAN (Examples)
• FDboost Boosting Functional Regression Models on CRAN and github
• cAIC4 Conditional Akaike Information Criterion for ‘lme4’ on CRAN and github
• iboost Inference for Model-based Boosting on github (Examples)
• coinflibs Conditional Inference after Likelihood-based Selection on github (Examples)
• effortless efficient operations on row-wise tensor product linked evaluations with special structures on github

References

1. 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 (PAKDD ’22), Springer International Publishing.
2. Liew BXW, Rügamer D, Duffy K, Taylor M, Jackson J (2021) The mechanical energetics of walking across the adult lifespan. PloS one 16, e0259817.
   link.
3. 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.
   link|pdf.
4. Weber T, Ingrisch M, Fabritius M, Bischl B, Rügamer D (2021) Survival-oriented embeddings for improving accessibility to complex data structures. NeurIPS 2021, Bridging the Gap: From Machine Learning Research to Clinical Practice.
   link|pdf.
5. Weber T, Ingrisch M, Bischl B, Rügamer D (2021) Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation. NeurIPS 2021, Deep Generative Models and Downstream Applications.
   link|pdf.
6. Rügamer D, Baumann PFM, Kneib T, Hothorn T (2021) Transforming Autoregression: Interpretable and Expressive Time Series Forecast. arXiv:2110.08248 [cs, stat].
   link|pdf.
7. 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.
8. Schalk D, Bischl B, Rügamer D (2021) Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization.
   link | pdf .
9. 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),
10. 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), pp. 1–6.
    link|pdf.
11. Rügamer D, Baumann P, Greven S (2021) Selective Inference for Additive and Mixed Models. Computational Statistics and Data Analysis.
    link|pdf.
12. Rezaei M, Dorigatti E, Rügamer D, Bischl B (2021) Learning Statistical Representation with Joint Deep Embedded Clustering. arXiv preprint arXiv:2109.05232.
13. 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.
14. *Coors S, *Schalk D, Bischl B, Rügamer D (2021) Automatic Componentwise Boosting: An Interpretable AutoML System. ECML-PKDD Workshop on Automating Data Science.
    link | pdf .
15. Berninger C, Stöcker A, Rügamer D (2021) A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction. Journal of Forecasting.
    link|pdf.
16. 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.
    link|pdf.
17. 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.
    link|pdf.
18. Rügamer D, Shen R, Bukas C et al. (2021) deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. arXiv:2104.02705 [stat].
    link|pdf.
19. Liew B, Lee HY, Rügamer D et al. (2021) A novel metric of reliability in pressure pain threshold measurement. Scientific Reports (Nature).
20. 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.
    link | pdf.
21. Fritz C, Dorigatti E, Rügamer D (2021) Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. arXiv:2101.00661 [cs, stat].
    link|pdf.
22. Rügamer D, Pfisterer F, Bischl B (2020) Neural Mixture Distributional Regression. arXiv:2010.06889 [cs, stat].
    link|pdf.
23. Rügamer D, Kolb C, Klein N (2020) A Unified Network Architecture for Semi-Structured Deep Distributional Regression. arXiv:2002.05777 [cs, stat].
    link|pdf.
24. Rügamer D, Greven S (2020) Inference for L2-Boosting. Statistics and Computing 30, 279–289.
    link|pdf.
25. Liew BXW, Rügamer D, Stöcker A, De Nunzio AM (2020) Classifying neck pain status using scalar and functional biomechanical variables – development of a method using functional data boosting. Gait & Posture 75, 146–150.
    link.
26. Liew B, Rügamer D, De Nunzio A, Falla D (2020) Interpretable machine learning models for classifying low back pain status using functional physiological variables. European Spine Journal 29, 1845–1859.
    link.
27. Liew BXW, Rügamer D, Abichandani D, De Nunzio AM (2020) Classifying individuals with and without patellofemoral pain syndrome using ground force profiles – Development of a method using functional data boosting. Gait & Posture 80, 90–95.
    link.
28. Liew BXW, Peolsson A, Rügamer D et al. (2020) Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy – a machine learning approach. Scientific Reports.
    link.
29. Brockhaus S, Rügamer D, Greven S (2020) Boosting Functional Regression Models with FDboost. Journal of Statistical Software 94, 1–50.
30. Rügamer D, Brockhaus S, Gentsch K, Scherer K, Greven S (2018) Boosting factor-specific functional historical models for the detection of synchronization in bioelectrical signals. Journal of the Royal Statistical Society: Series C (Applied Statistics) 67, 621–642.
31. Rügamer D, Greven S (2018) Selective inference after likelihood-or test-based model selection in linear models. Statistics & Probability Letters 140, 7–12.
32. Säfken B, Rügamer D, Kneib T, Greven S (2018) Conditional model selection in mixed-effects models with caic4. to appear in the Journal of Statistical Software.
33. Brockhaus S, Rügamer D, Greven S (2017) Boosting Functional Regression Models with FDboost. to appear in the Journal of Statistical Software.
34. Klüser L, Holler PJ, Simak J et al. (2016) Predictors of sudden cardiac death in Doberman Pinschers with dilated cardiomyopathy. Journal of veterinary internal medicine 30, 722–732.
35. Gillhuber J, Rügamer D, Pfister K, Scheuerle MC (2014) Giardiosis and other enteropathogenic infections: a study on diarrhoeic calves in Southern Germany. BMC research notes 7, 112.