Martin Binder

About

I am a PhD scientist at the chair of Statistical Learning and Data Science at LMU Munich, mostly working on automatic machine learning, hyperparameter optimization, and related topics. My main focus there is on black-box optimization methods such as Bayesian optimization, and I work on making these more efficient using multi-fidelity approaches and transfer-learning.

I am also working on deep learning, specifically self-supervised learning, for genome sequence classification and analysis.

I am the main author of mlrCPO (deprecated) and mlr3pipelines (the new replacement), both R software packages for data preprocessing within machine learning pipelines.

Before finding my way into Computational Statistics and Machine Learning, I obtained an MMath degree in Theoretical Physics and an MSc degree in Biostatistics.

Contact

Feel free to write me emails; I don’t care much about formality so you can be be as informal as you like. You can also reach me on the LMU Mattermost Server, I am @mb706 – I greatly prefer Mattermost conversation to email. If you want to contact me (or other developers) specifically for mlr3 / mlr3pipelines / mlr3torch, you can join our developer Mattermost server through this invite link.

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 148, 1st floor

Phone: +49 89 2180 3521

martin.binder [at] stat.uni-muenchen.de

You Can Find me on

References

  1. Karl F, Pielok T, Moosbauer J et al. (2022) Multi-Objective Hyperparameter Optimization – An Overview. arXiv preprint arXiv:2206.07438.
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  2. Pfisterer F, Schneider L, Moosbauer J, Binder M, Bischl B (2022) YAHPO Gym – An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization First Conference on Automated Machine Learning (Main Track),
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  3. 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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  4. Moosbauer J, Binder M, Schneider L et al. (2021) Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers.
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  5. Binder M, Pfisterer F, Lang M, Schneider L, Kotthoff L, Bischl B (2021) mlr3pipelines - Flexible Machine Learning Pipelines in R. Journal of Machine Learning Research 22, 1–7.
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  6. Schneider L, Pfisterer F, Binder M, Bischl B (2021) Mutation is all you need 8th ICML Workshop on Automated Machine Learning,
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  7. Becker M, Binder M, Bischl B et al. (2021) mlr3 book.
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  8. Dandl S, Molnar C, Binder M, Bischl B (2020) Multi-Objective Counterfactual Explanations. In: In: Bäck T , In: Preuss M , In: Deutz A et al. (eds) Parallel Problem Solving from Nature – PPSN XVI, pp. 448–469. Springer International Publishing, Cham.
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  9. Binder M, Pfisterer F, Bischl B (2020) Collecting Empirical Data About Hyperparameters for Data Driven AutoML AutoML Workshop at ICML 2020,
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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. Lang M, Binder M, Richter J et al. (2019) mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software 4, 1903.
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