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
- Hyperparameter Tuning & Automatic Machine Learning
- Zero-Shot Hyperparameter Optimization
- Algorithmic Fairness
- Benchmarking
You Can Find me on
References
- 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.
- Schneider L, Pfisterer F, Kent P, Branke J, Bischl B, Thomas J (2022) Tackling Neural Architecture Search With Quality Diversity Optimization.
link|pdf. - 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.
- 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.
- Sonabend R, Pfisterer F, Mishler A, Schauer M, Burk L, Vollmer S (2022) Flexible Group Fairness Metrics for Survival Analysis.
link|pdf. - 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.
link|pdf. - 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.
link|pdf. - Pfisterer F, Schneider L, Moosbauer J, Binder M, Bischl B (2022) YAHPO Gym - Design Criteria and a new Multifidelity Benchmark for Hyperparameter Optimization.
link | pdf. - 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).
arxiv | pdf. - 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.
link. - 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).
- Schneider L, Pfisterer F, Binder M, Bischl B (2021) Mutation is all you need. arXiv preprint arXiv:2107.07343.
link | pdf. - Rügamer D, Shen R, Bukas C, Sousa LB de Andrade e, Thalmeier D, Klein N, Kolb C, Pfisterer F, Kopper P, Bischl B, Müller C (2021) deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression. arXiv:2104.02705 [stat].
link|pdf. - 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.
link | pdf. - Funk H, Becker C, Hofheinz A, Xi G, Zhang Y, Pfisterer F, Weigert M, Mittermeier M (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.
- Binder M, Pfisterer F, Bischl B (2020) Collecting Empirical Data About Hyperparameters for Data Driven AutoML AutoML Workshop at ICML 2020,
pdf. - Agrawal A, Pfisterer F, Bischl B, Chen J, Sood S, Shah S, Buet-Golfouse F, Mateen BA, Vollmer S (2020) Debiasing classifiers: is reality at variance with expectation?
link|pdf. - Rügamer D, Pfisterer F, Bischl B (2020) Neural Mixture Distributional Regression. arXiv preprint arXiv:2010.06889.
link. - 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.
link | pdf. - Pfisterer F, Thomas J, Bischl B (2019) Towards Human Centered AutoML. arXiv preprint arXiv:1911.02391.
link | pdf. - 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.
link | pdf. - Pfisterer F, Coors S, Thomas J, Bischl B (2019) Multi-Objective Automatic Machine Learning with AutoxgboostMC. arXiv preprint arXiv:1908.10796.
link | pdf. - Lang M, Binder M, Richter J, Schratz P, Pfisterer F, Coors S, Au Q, Casalicchio G, Kotthoff L, Bischl B (2019) mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software 4, 1903.
link | pdf. - 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.
link | pdf . - Rijn JN van, Pfisterer F, Thomas J, Bischl B, Vanschoren J (2018) Meta Learning for Defaults–Symbolic Defaults Neurips 2018 Workshop on Meta Learning,
link | pdf.