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. Dandl S, Pfisterer F, Bischl B (0AD) Multi-Objective Counterfactual Fairness GECCO ’22: Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM, Boston, United States of America.
  2. Schneider L, Pfisterer F, Kent P, Branke J, Bischl B, Thomas J (2022) Tackling Neural Architecture Search With Quality Diversity Optimization.
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  3. Pfisterer F, Harbron C, Jansen G, Xu T (2022) Evaluating Domain Generalization for Survival Analysis in Clinical Studies. In: In: Flores G , In: Chen GH , In: Pollard T , In: Ho JC , In: Naumann T (eds) Proceedings of the Conference on Health, Inference, and Learning, pp. 32–47. PMLR. https://proceedings.mlr.press/v174/pfisterer22a.html.
  4. Schneider L, Pfisterer F, Thomas J, Bischl B (2022) A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models GECCO ’22: Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM, Boston, United States of America.
  5. Sonabend R, Pfisterer F, Mishler A, Schauer M, Burk L, Vollmer S (2022) Flexible Group Fairness Metrics for Survival Analysis.
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  6. Moosbauer J, Binder M, Schneider L et al. (2022) Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers.
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  7. 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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  8. Pfisterer F, Schneider L, Moosbauer J, Binder M, Bischl B (2022) YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization.
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  9. Pfisterer F, Rijn JN van, Probst P, Müller A, Bischl B (0AD) Learning Multiple Defaults for Machine Learning Algorithms. 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21 Companion).
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  10. Pfisterer F, Kern C, Dandl S, Sun M, Kim MP, Bischl B (2021) mcboost: Multi-Calibration Boosting for R. Journal of Open Source Software 6, 3453.
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  11. Gijsbers P, Pfisterer F, Rijn JN van, Bischl B, Vanschoren J (2021) Meta-Learning for Symbolic Hyperparameter Defaults. 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21 Companion).
  12. Schneider L, Pfisterer F, Binder M, Bischl B (2021) Mutation is all you need. arXiv preprint arXiv:2107.07343.
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  13. Rügamer D, Shen R, Bukas C et al. (2021) deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. arXiv:2104.02705 [stat].
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  14. 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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  15. Funk H, Becker C, Hofheinz A et al. (2021) Towards an automated classification of Hess & Brezowsky’s atmospheric circulation patterns Tief and Trog Mitteleuropa using Deep Learning Methods Environmental Informatics – A bogeyman or saviour to achieve the UN Sustainable Development Goals? - Adjunct Proceedings of the 35th edition of the EnviroInfo – the long standing and established international and interdisciplinary conference series on leading environmental information and communication technologies, pp. 22–30. Shaker Verlag GmbH. https://doi.org/10.2370/9783844083293.
  16. 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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  17. Agrawal A, Pfisterer F, Bischl B et al. (2020) Debiasing classifiers: is reality at variance with expectation?
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  18. Rügamer D, Pfisterer F, Bischl B (2020) Neural Mixture Distributional Regression. arXiv preprint arXiv:2010.06889.
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  19. Sun X, Bommert A, Pfisterer F, Rahnenfürher J, Lang M, Bischl B (2020) High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions. In: In: Bi Y , In: Bhatia R , In: Kapoor S (eds) Intelligent Systems and Applications, pp. 629–647. Springer International Publishing, Cham.
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  20. Pfisterer F, Thomas J, Bischl B (2019) Towards Human Centered AutoML. arXiv preprint arXiv:1911.02391.
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  21. Pfisterer F, Beggel L, Sun X, Scheipl F, Bischl B (2019) Benchmarking time series classification – Functional data vs machine learning approaches. arXiv preprint arXiv:1911.07511.
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  22. 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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  23. 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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  24. Schiffner J, Bischl B, Lang M et al. (2016) mlr Tutorial.
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  25. 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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