Marc Becker

About

I am working on the mlr3 project as a research software engineer and mainly responsible for the optimization packages.

I obtained a Bachelor's Degree (B.Sc.) in Geography from the Freie Universität Berlin and a Master's Degree (M.Sc.) in Geoinformatics from the Friedrich-Schiller-Universität Jena.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

marc.becker [at] stat.uni-muenchen.de

Research Interests

You Can Find me on

Software (Selection)

References

  1. Becker M, Schneider L, Binder M, Kotthoff L, Bischl B (2026) Mlr3mbo: Bayesian Optimization in R. arXiv preprint arXiv:2603.29730.
  2. Zobolas J, George A-M, López A, Fischer S, Becker M, Aittokallio T (2026) Prognostic Biomarker Discovery in Pancreatic Cancer through Hybrid Ensemble Feature Selection and Multi-Omics Data. BioData Mining.
  3. Fischer S, Zobolas J, Sonabend R, Becker M, Lang M, Binder M, Schneider L, Burk L, Schratz P, Jaeger BC, others (2025) Mlr3extralearners: Expanding the Mlr3 Ecosystem with Community-Driven Learner Integration. Journal of Open Source Software 10, 8331.
  4. Becker M, Schneider L, Fischer S (2024) Hyperparameter Optimization Applied Machine Learning Using mLr3 in R, pp. 85–115. Chapman and Hall/CRC.
  5. Binder M, Pfisterer F, Becker M, Wright MN (2024) Non-Sequential Pipelines and Tuning Applied Machine Learning Using Mlr3 in R, pp. 174–195. Chapman and Hall/CRC.
  6. Dandl S, Becker M, Bischl B, Casalicchio G, Bothmann L (2024) Mlr3summary: Concise and Interpretable Summaries for Machine Learning Models. arXiv preprint arXiv:2404.16899.
  7. Fischer S, Lang M, Becker M (2024) Large-Scale Benchmarking Applied Machine Learning Using Mlr3 in R, pp. 240–258. Chapman and Hall/CRC.
  8. Schneider L, Becker M (2024) Advanced Tuning Methods and Black Box Optimization Applied Machine Learning Using Mlr3 in R, pp. 116–145. Chapman and Hall/CRC.
  9. Schratz P, Becker M, Lang M, Brenning A (2024) Mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R. Journal of Statistical Software 111, 1–36.
  10. Bischl B, Binder M, Lang M, Pielok T, Richter J, Coors S, Thomas J, Ullmann T, Becker M, Boulesteix A-L, others (2023) Hyperparameter Optimization: Foundations, Algorithms, Best Practices, and Open Challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 13, e1484.
  11. Kuehn E, Becker M, Harpke A, Kühn I, Kuhlicke C, Schmitt T, Settele J, Musche M (2022) The Benefits of Counting Butterflies: Recommendations for a Successful Citizen Science Project. Ecology and Society 27.
  12. Moosbauer J, Binder M, Schneider L, Pfisterer F, Becker M, Lang M, Kotthoff L, Bischl B (2022) Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers. IEEE Transactions on Evolutionary Computation 26, 1336–1350.