Boosting

This group focuses on methodological research and application of gradient boosting. Topics include

• Uncertainty Quantification in Gradient Boosting

• Bayesian Boosting

• Variants of (component-wise) gradient boosting that facilitate interpretability

• Multiclass Boosting

• Boosting for Survival

Members

Publications

1. Gertheiss J, Rügamer D, Liew B, Greven S (2023) Functional Data Analysis: An Introduction and Recent Developments.
   link.
2. Schalk D, Bischl B, Rügamer D (2022) Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization. Journal of Computational and Graphical Statistics.
   link | pdf .
3. Pfisterer F, Kern C, Dandl S, Sun M, Kim MP, Bischl B (2021) mcboost: Multi-Calibration Boosting for R. Journal of Open Source Software 6, 3453.
   link.
4. *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 .
5. 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.
6. 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.
7. Brockhaus S, Rügamer D, Greven S (2020) Boosting Functional Regression Models with FDboost. Journal of Statistical Software 94, 1–50.
8. Rügamer D, Greven S (2020) Inference for L2-Boosting. Statistics and Computing 30, 279–289.
   link|pdf.
9. Au Q, Schalk D, Casalicchio G, Schoedel R, Stachl C, Bischl B (2019) Component-Wise Boosting of Targets for Multi-Output Prediction. arXiv preprint arXiv:1904.03943.
   link | pdf.
10. Schalk D, Thomas J, Bischl B (2018) compboost: Modular Framework for Component-Wise Boosting. JOSS 3, 967.
    link | pdf.
11. Thomas J, Coors S, Bischl B (2018) Automatic Gradient Boosting. ICML AutoML Workshop.
    link | pdf.
12. Thomas J, Mayr A, Bischl B, Schmid M, Smith A, Hofner B (2018) Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates. Statistics and Computing 28, 673–687.
    link | pdf.
13. Thomas J, Hepp T, Mayr A, Bischl B (2017) Probing for sparse and fast variable selection with model-based boosting. Computational and mathematical methods in medicine 2017.
    link | pdf.
14. Casalicchio G, Tutz G, Schauberger G (2015) Subject-specific Bradley–Terry–Luce models with implicit variable selection. Statistical Modelling 15, 526–547.
    link | pdf.