Florian Pfisterer

About

Since April 2018 I am a PhD student at the working group for Computational Statistics at the Ludwig-Maximilians-University Munich. I am also a member of the Munich Center for Machine Learning (MCML).

I obtained a Bachelor's Degree (B.Sc.) and Master's Degree (M.Sc.) in Statistics from the Ludwig-Maximilians-University Munich.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 040

Phone: +49 89 2180 2763

florian.pfisterer [at] stat.uni-muenchen.de

Research Interests

You Can Find me on

References

  1. 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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  2. Rügamer D, Pfisterer F, Bischl B (2020) Neural Mixture Distributional Regression. arXiv preprint arXiv:2010.06889.
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  3. Pfisterer F, Thomas J, Bischl B (2019) Towards Human Centered AutoML.
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  4. Pfisterer F, Beggel L, Sun X, Scheipl F, Bischl B (2019) Benchmarking time series classification – Functional data vs machine learning approaches.
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  5. Pfisterer F, Coors S, Thomas J, Bischl B (2019) Multi-Objective Automatic Machine Learning with AutoxgboostMC.
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  6. 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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  7. Sun X, Bommert A, Pfisterer F, Rähenfürher J, Lang M, Bischl B (2019) High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions Intelligent Systems and Applications - Proceedings of the 2019 Intelligent Systems Conference, IntelliSys 2019, London, UK, September 5-6, 2019, Volume 1, pp. 629–647. https://doi.org/10.1007/978-3-030-29516-5_48.
  8. Pfisterer F, Rijn JN van, Probst P, Müller A, Bischl B (2018) Learning Multiple Defaults for Machine Learning Algorithms. stat 1050, 23.
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  9. Schiffner J, Bischl B, Lang M et al. (2016) mlr Tutorial.
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  10. 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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