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Florian Pfisterer
Florian Pfisterer
PhD Candidate, Ludwig Maximilian University of Munich
Bestätigte E-Mail-Adresse bei stat.uni-muenchen.de
Titel
Zitiert von
Zitiert von
Jahr
mlr3: A modern object-oriented machine learning framework in R
M Lang, M Binder, J Richter, P Schratz, F Pfisterer, S Coors, Q Au, ...
Journal of Open Source Software 4 (44), 1903, 2019
1482019
Learning multiple defaults for machine learning algorithms
F Pfisterer, JN van Rijn, P Probst, AC Müller, B Bischl
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021
272021
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features
F Pargent, F Pfisterer, J Thomas, B Bischl
Computational Statistics 37 (5), 2671-2692, 2022
162022
mlr3pipelines—Flexible machine learning pipelines in r
M Binder, F Pfisterer, M Lang, L Schneider, L Kotthoff, B Bischl
The Journal of Machine Learning Research 22 (1), 8314-8320, 2021
162021
mlr3: A modern object-oriented machine learning framework in RJ Open Source Softw
M Lang, M Binder, J Richter, P Schratz, F Pfisterer, S Coors, Q Au, ...
162019
Debiasing classifiers: is reality at variance with expectation?
A Agrawal, F Pfisterer, B Bischl, F Buet-Golfouse, S Sood, J Chen, S Shah, ...
arXiv preprint arXiv:2011.02407, 2020
142020
Yahpo gym-an efficient multi-objective multi-fidelity benchmark for hyperparameter optimization
F Pfisterer, L Schneider, J Moosbauer, M Binder, B Bischl
International Conference on Automated Machine Learning, 3/1-39, 2022
132022
Benchmarking time series classification--Functional data vs machine learning approaches
F Pfisterer, L Beggel, X Sun, F Scheipl, B Bischl
arXiv preprint arXiv:1911.07511, 2019
112019
Multi-objective automatic machine learning with autoxgboostmc
F Pfisterer, S Coors, J Thomas, B Bischl
arXiv preprint arXiv:1908.10796, 2019
102019
High dimensional restrictive federated model selection with multi-objective bayesian optimization over shifted distributions
X Sun, A Bommert, F Pfisterer, J Rähenfürher, M Lang, B Bischl
Intelligent Systems and Applications: Proceedings of the 2019 Intelligent …, 2020
92020
Meta learning for defaults: Symbolic defaults
JN van Rijn, F Pfisterer, J Thomas, A Muller, B Bischl, J Vanschoren
Neural Information Processing Workshop on Meta-Learning, 2018
92018
Meta-learning for symbolic hyperparameter defaults
P Gijsbers, F Pfisterer, JN van Rijn, B Bischl, J Vanschoren
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021
82021
mlr Tutorial
J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ...
arXiv preprint arXiv:1609.06146, 2016
82016
Automated benchmark-driven design and explanation of hyperparameter optimizers
J Moosbauer, M Binder, L Schneider, F Pfisterer, M Becker, M Lang, ...
IEEE Transactions on Evolutionary Computation 26 (6), 1336-1350, 2022
62022
mcboost: Multi-Calibration Boosting for R
F Pfisterer, C Kern, S Dandl, M Sun, MP Kim, B Bischl
Journal of Open Source Software 6 (64), 3453, 2021
62021
mlr3book
M Becker, M Binder, B Bischl, N Foss, L Kotthoff, M Lan, F Pfisterer, ...
URl: https://mlr3book. mlr-org. com 28, 29-30, 2021
62021
Collecting empirical data about hyperparameters for data driven AutoML
M Binder, F Pfisterer, B Bischl
Democratizing Machine Learning Contributions in AutoML and Fairness, 93, 2020
62020
Towards human centered AutoML
F Pfisterer, J Thomas, B Bischl
arXiv preprint arXiv:1911.02391, 2019
62019
Deepregression: a flexible neural network framework for semi-structured deep distributional regression
D Rügamer, C Kolb, C Fritz, F Pfisterer, P Kopper, B Bischl, R Shen, ...
arXiv preprint arXiv:2104.02705, 2021
52021
Mutation is all you need
L Schneider, F Pfisterer, M Binder, B Bischl
arXiv preprint arXiv:2107.07343, 2021
42021
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