Publications, Software and Workshops

Publications

• Coors, Schalk, Bischl, Rügamer (2021): AutoCompboost: Automatic Interpretable Boosting. Accepted at ECML-PKDD Workshop on Automating Data Science (ADS2021)
• Rügamer, et al. (2021): deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression.
• Liew, Lee, Rügamer, Nunzio, Heneghan, Falla, Evans (2021): A novel metric of reliability in pressure pain threshold measurement. Accepted at Scientific Reports - Nature
• Goschenhofer, Hvingelby Rügamer, Thomas, Wagner, Bischl (2021): Deep Semi-Supervised Learning for Time Series Classification. Under Revision.
• Fritz, Dorigatti, Rügamer (2021): Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany. Under Revision.
• Kopper, Pölsterl, Wachinger, Bischl, Bender, Rügamer (2020): Semi-Structured Deep Piecewise Exponential Models. Accepted at the AAAI Spring Symposium 2021.
• Baumann, Hothorn, Rügamer (2020): Deep Conditional Transformation Models. Accepted at ECML-PKDD 2021.
• Liew, Peolsson, Rügamer, Wibault, Löfgren, Dedering, Zsigmond, Falla (2020): Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy - a machine learning approach. Accepted at Scientific Reports - Nature
• Rügamer, Pfisterer, Bischl (2020): Neural Mixture Density Regression. Under Revision.
• Bender, Rügamer, Scheipl, Bischl (2020): A General Machine Learning Framework for Survival Analysis. ECML-PKDD 2020.
• Berninger, Stöcker, Rügamer (2020): A Bayesian Time-Varying Autoregressive Model for Improved Short- and Long-Term Prediction. Accepted at the Journal of Forecasting.
• Rügamer, Baumann, Greven (2020): Selective Inference for Additive and Mixed Models. Under Revision.
• Rügamer, Kolb and Klein (2020): A Unified Network Architecture for Semi-Structured Deep Distributional Regression. Under Revision.
• Liew, Rügamer, Abichandani, De Nunzio (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
• Liew, Rügamer, De Nunzio, Falla (2020): Interpretable machine learning models for classifying low back pain status using functional physiological variables. European Spine Journal 29, 1845 -- 1859.
• Liew, Rügamer, Stöcker, De Nunzio (2020): Classifying neck pain status using scalar and functional biomechanical variables - development of a method using functional data boosting. Gait and Posture 76, 146-150.
• Rügamer, Greven (2020): Inference for L2-Boosting. Statistics and Computing volume 30, 279-289.
• Brockhaus, Rügamer, Greven (2020): Boosting Functional Regression Models with FDboost. Journal of Statistical Software, 94 (10), 1-50.
• Rügamer, Greven (2018): Selective inference after likelihood- or test-based model selection in linear models. Statistics & Probability Letters. Volume 140, Pages 7-12.
• Säfken, Rügamer, Kneib, Greven (2018): Conditional Model Selection in Mixed-Effects Models with cAIC4. To appear in the Journal of Statistical Software.
• Rügamer, Brockhaus, Gentsch, Scherer, Greven (2018): Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals. Journal of the Royal Statistical Society, Series C, 67: 621-642.
• Klüser, Holler, Simak, Tater, Smets, Rügamer, Küchenhoff, Wess (2016): Predictors of Sudden Cardiac Death in Doberman Pinschers with Dilated Cardiomyopathy. Journal of Veterinary Internal Medicine, volume 3, number 3, 722--732.
• Gillhuber, Rügamer, Pfister, Scheuerle (2014). Giardiosis and other enteropathogenic infections: a study on diarrhoeic calves in Southern Germany. BMC Research Notes, 7, 112.

Software (R-packages)

• deepregression Semi-Strutured Deep Distrobutional Regression on Github
• selfmade SELective inference For Mixed and ADditive model Estimators on Github and soon on CRAN.
• 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
• coinflibs Conditional Inference after Likelihood-based Selection on Github
• effortless efficient operations on row-wise tensor product linked evaluations with special structures on Github

Talks, Workshops and Research Stays

• 18. March ‘21 (Leuven, BE) Sampling-based Approaches for Valid Post-Selection Inference Seminar Series in Statistics, KU Leuven
• 28. October ‘20 (Dortmund, GER) Semi-Structured Deep Distributional Regression. Regression approaches for large-scale high-dimensional data, Dortmund Data Science Centers
• 15. October ‘20 (Bonn, GER) Semi-Structured Deep Distributional Regression. (Virtual) Institutskolloquium IMBIE
• 29. July ‘20 (Munich, GER) Semi-Structured Deep Distributional Regression. Virtual Workshop by the Munich Center of Machine Learning
• 26. February ‘20 (Munich, GER) Semi-Structured Deep Distributional Learning. Institutskolloquium, Department of Statistics, LMU Munich
• January ‘20 (Xiamen, CHN) Short research stay and exchange with several people involved in the DFG RTG group High Dimensional Nonstationary Time Series
• 21. March ‘19 (Munich, GER) Inference for L2-Boosting, DAGSTAT 2019, Machine Learning Session
• 15. June ‘18 (Munich, GER) Estimation, Model Choice and Subsequent Inference: Methods for Additive and Functional Regression Models. Doctoral Dissertation Defense
• 26. - 28. March 2018 (Frankfurt a.M., GER) Boosting Factor-Specific Functional Historical Models. Award Session, 64th Biometric Colloquium
• 16. - 18. December 2017 (London, UK) Selective Inference for L2-Boosting. 10th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2017)
• 23. - 25. July 2017 (Reisensburg, GER) Boosting functional regression models with FDboost. Statistical Computing 2017
• 9. - 11. December 2016 (Seville, ESP) Boosting Factor-Specific Functional Historical Models. 9th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2016)
• 30. November 2016 (Munich, GER) Selective Inference for L2-Boosting. Institutskolloquium, Department of Statistics LMU, Munich