Janek Thomas

About

I am PhD student at the Department of Statistics at the Ludwig-Maximilians-University Munich (LMU) for the working group Computational Statistics. I have a Bachelor's Degree (B.Sc.) in Statistics with a minor in Computer Science and continued with a Master's Degree (M.Sc) in Statistics here at the LMU.

You can find more information about me on my personal website.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Raum L344

+49 89 2180 3196

Janek.Thomas [at] stat.uni-muenchen.de

Research Interests

You Can Find me on

Membership

References

  1. Thomas J, Hepp T, Mayr A, Bischl B (2018) Probing for sparse and fast variable selection with model-based boosting.
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  2. 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.
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  3. Kühn D, Probst P, Thomas J, Bischl B (2018) Automatic Exploration of Machine Learning Experiments on OpenML. arXiv preprint arXiv:1806.10961.
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  4. Thomas J, Coors S, Bischl B (2018) Automatic Gradient Boosting. ICML AutoML Workshop.
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  5. Schalk D, Thomas J, Bischl B (2018) compboost: Modular Framework for Component-Wise Boosting. The Journal of Open Source Software, 967.
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  6. Bischl B, Richter J, Bossek J, Horn D, Thomas J, Lang M (2017) mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions. arXiv preprint arXiv:1703.03373.
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  7. Kotthaus H, Richter J, Lang A et al. (2017) RAMBO: Resource-Aware Model-Based Optimization with Scheduling for Heterogeneous Runtimes and a Comparison with Asynchronous Model-Based Optimization International Conference on Learning and Intelligent Optimization, pp. 180–195. Springer.
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  8. Rietzler M, Geiselhart F, Thomas J, Rukzio E (2016) FusionKit: a generic toolkit for skeleton, marker and rigid-body tracking Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 73–84. ACM.
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  9. Schiffner J, Bischl B, Lang M et al. (2016) mlr Tutorial.
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