Janek Thomas

About

I finished my PhD at the Working Group Computational Statistics (now called Chair of Statistical Learning & Data Science) in April 2019 focusing on Automated Machine Learning and Gradient Boosting. During my PhD I was a research intern at the Microsoft Cloud and Information Services Lab and H2O.ai.

After my PhD I was group lead AutoML & XAI at the Fraunhofer Institute of Integrated Circuits (IIS) funded by the ADA Lovelace Center and deputy professor of the chair Computational Statistics at the Technical University Dortmund.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 344, 3rd floor

Phone: +49 89 2180 3196

janek.thomas [ähht] stat.uni-muenchen.de

Research Interests

You Can Find me on

Membership

References

  1. Bischl B, Binder M, Lang M et al. (2021) Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. arXiv preprint arXiv:2107.05847.
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  2. Goschenhofer J, Hvingelby R, Rügamer D, Thomas J, Wagner M, Bischl B (2021) Deep Semi-Supervised Learning for Time Series Classification. arXiv preprint arXiv:2102.03622.
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  3. Pargent F, Pfisterer F, Thomas J, Bischl B (2021) Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. arXiv preprint arXiv:2104.00629.
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  4. Gerostathopoulos I, Plášil F, Prehofer C, Thomas J, Bischl B (2021) Automated Online Experiment-Driven Adaptation–Mechanics and Cost Aspects. IEEE Access 9, 58079–58087.
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  5. Binder M, Moosbauer J, Thomas J, Bischl B (2020) Multi-Objective Hyperparameter Tuning and Feature Selection Using Filter Ensembles Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 471–479. Association for Computing Machinery, New York, NY, USA.
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  6. Goschenhofer J, Pfister FMJ, Yuksel KA, Bischl B, Fietzek U, Thomas J (2019) Wearable-based Parkinson’s Disease Severity Monitoring using Deep Learning Joint European Conference on Machine Learning and Knowledge Discovery in Databases,
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  7. Gijsbers P, LeDell E, Thomas J, Poirier S, Bischl B, Vanschoren J (2019) An Open Source AutoML Benchmark ICML AutoML Workshop,
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  8. Pfisterer F, Coors S, Thomas J, Bischl B (2019) Multi-Objective Automatic Machine Learning with AutoxgboostMC ECML PKDD 2019 Workshop on Automating Data Science (ADS 2019),
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  9. Pfisterer F, Thomas J, Bischl B (2019) Towards Human Centered AutoML. arXiv preprint arXiv:1911.02391.
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  10. 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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  11. 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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  12. Thomas J, Coors S, Bischl B (2018) Automatic Gradient Boosting ICML AutoML Workshop,
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  13. Schalk D, Thomas J, Bischl B (2018) compboost: Modular Framework for Component-Wise Boosting. Journal of Open Source Software 3, 967.
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  14. Rijn JN van, Pfisterer F, Thomas J, Muller A, Bischl B, Vanschoren J (2018) Meta Learning for Defaults–Symbolic Defaults Neural Information Processing Workshop on Meta-Learning,
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  15. 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, 8–pages.
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  16. 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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  17. 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, Cham.
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  18. Schiffner J, Bischl B, Lang M et al. (2016) mlr Tutorial.
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  19. 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.
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