Daniel Schalk

About

After previously being a student assistant at the working group Computational Statistics at the Ludwig-Maximilians-University Munich, I joined it as a PhD student in june 2018 with a main focus on machine learning.

I have a Bachelor's Degree (B.Sc.) in Business Mathematics and Actuarial Sciences from the University of Applied Sciences Rosenheim and a Master's Degree (M.Sc.) in Statistics from the LMU Munich.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 344, 3rd floor

Phone: +49 89 2180 3196

daniel.schalk [at] stat.uni-muenchen.de

Research Interests

You Can Find me on

References

  1. Schalk D, Hoffmann V, Bischl B, Mansmann U (2022) Distributed non-disclosive validation of predictive models by a modified ROC-GLM. arXiv preprint arXiv:2202.10828.
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  2. Schalk D, Bischl B, Rügamer D (2021) Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization. arXiv preprint arXiv:2110.03513.
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  3. *Coors S, *Schalk D, Bischl B, Rügamer D (2021) Automatic Componentwise Boosting: An Interpretable AutoML System. ECML-PKDD Workshop on Automating Data Science.
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  4. 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.
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  5. Schalk D, Thomas J, Bischl B (2018) compboost: Modular Framework for Component-Wise Boosting. JOSS 3, 967.
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