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. Gijsbers P, Bueno MLP, Coors S, LeDell E, Poirier S, Thomas J, Bischl B, Vanschoren J (2022) AMLB: an AutoML Benchmark. arXiv preprint arXiv:2207.12560.
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  2. Karl F, Pielok T, Moosbauer J, Pfisterer F, Coors S, Binder M, Schneider L, Thomas J, Richter J, Lang M, others (2022) Multi-Objective Hyperparameter Optimization – An Overview. arXiv preprint arXiv:2206.07438.
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  3. Schneider L, Pfisterer F, Thomas J, Bischl B (2022) A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 2136–2142. Association for Computing Machinery, New York, NY, USA.
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  4. Pargent F, Pfisterer F, Thomas J, Bischl B (2022) Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Computational Statistics, 1–22.
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  5. Schneider L, Pfisterer F, Kent P, Branke J, Bischl B, Thomas J (2022) Tackling Neural Architecture Search With Quality Diversity Optimization International Conference on Automated Machine Learning, pp. 9–1. PMLR.
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  6. Goschenhofer J, Hvingelby R, Rügamer D, Thomas J, Wagner M, Bischl B (2021) Deep Semi-Supervised Learning for Time Series Classification 20th IEEE International Conference on Machine Learning and Applications (ICMLA),
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  7. Bischl B, Binder M, Lang M, Pielok T, Richter J, Coors S, Thomas J, Ullmann T, Becker M, Boulesteix A-L, Deng D, Lindauer M (2021) Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. arXiv preprint arXiv:2107.05847.
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  8. 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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  9. Kaminwar SR, Goschenhofer J, Thomas J, Thon I, Bischl B (2021) Structured Verification of Machine Learning Models in Industrial Settings. Big Data.
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  10. 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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  11. Pfisterer F, Thomas J, Bischl B (2019) Towards Human Centered AutoML. arXiv preprint arXiv:1911.02391.
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  12. Pfisterer F, Coors S, Thomas J, Bischl B (2019) Multi-Objective Automatic Machine Learning with AutoxgboostMC. arXiv preprint arXiv:1908.10796.
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  13. Gijsbers P, LeDell E, Thomas J, Poirier S, Bischl B, Vanschoren J (2019) An Open Source AutoML Benchmark. CoRR abs/1907.00909.
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  14. Pfister FMJ, Schumann A von, Bemetz J, Thomas J, Ceballos-Baumann A, Bischl B, Fietzek U (2019) Recognition of subjects with early-stage Parkinson from free-living unilateral wrist-sensor data using a hierarchical machine learning model JOURNAL OF NEURAL TRANSMISSION, pp. 663–663. SPRINGER WIEN SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA.
  15. Goschenhofer J, Pfister FMJ, Yuksel KA, Bischl B, Fietzek U, Thomas J (2019) Wearable-based Parkinson’s Disease Severity Monitoring using Deep Learning. Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, 400–415.
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  16. Rijn JN van, Pfisterer F, Thomas J, Bischl B, Vanschoren J (2018) Meta Learning for Defaults–Symbolic Defaults NeurIPS 2018 Workshop on Meta Learning,
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  17. 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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  18. 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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  19. Thomas J, Coors S, Bischl B (2018) Automatic Gradient Boosting. ICML AutoML Workshop.
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  20. Schalk D, Thomas J, Bischl B (2018) compboost: Modular Framework for Component-Wise Boosting. JOSS 3, 967.
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  21. 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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  22. Kotthaus H, Richter J, Lang A, Thomas J, Bischl B, Marwedel P, Rahnenführer J, Lang M (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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  23. 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.
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  24. 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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  25. Schiffner J, Bischl B, Lang M, Richter J, Jones ZM, Probst P, Pfisterer F, Gallo M, Kirchhoff D, Kühn T, Thomas J, Kotthoff L (2016) mlr Tutorial.
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