Florian Pfisterer
Florian Pfisterer
Dirección de correo verificada de stat.uni-muenchen.de
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Año
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
342019
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
112021
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, ...
72019
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
Proceedings of SAI Intelligent Systems Conference, 629-647, 2019
62019
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
62018
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
42019
Neural Mixture Distributional Regression
D Rügamer, F Pfisterer, B Bischl
arXiv preprint arXiv:2010.06889, 2020
32020
Multi-objective automatic machine learning with autoxgboostmc
F Pfisterer, S Coors, J Thomas, B Bischl
arXiv preprint arXiv:1908.10796, 2019
32019
mlr Tutorial
J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ...
arXiv preprint arXiv:1609.06146, 2016
32016
Debiasing classifiers: is reality at variance with expectation?
A Agrawal, F Pfisterer, B Bischl, J Chen, S Sood, S Shah, F Buet-Golfouse, ...
Available at SSRN 3711681, 2020
22020
mlr3 book
M Becker, M Binder, B Bischl, M Lang, F Pfisterer, NG Reich, J Richter, ...
URl: https://mlr3book. mlr-org. com, 2021
12021
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
D Rügamer, R Shen, C Bukas, D Thalmeier, N Klein, C Kolb, F Pfisterer, ...
arXiv preprint arXiv:2104.02705, 2021
12021
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features
F Pargent, F Pfisterer, J Thomas, B Bischl
arXiv preprint arXiv:2104.00629, 2021
12021
Collecting Empirical Data About Hyperparameters for Data Driven AutoML
M Binder, F Pfisterer, B Bischl
Proceedings of the 7th ICML Workshop on Automated Machine Learning (AutoML 2020), 2020
12020
Towards Human Centered AutoML
F Pfisterer, J Thomas, B Bischl
arXiv preprint arXiv:1911.02391, 2019
12019
Mutation is all you need
L Schneider, F Pfisterer, M Binder, B Bischl
arXiv preprint arXiv:2107.07343, 2021
2021
Meta-Learning for Symbolic Hyperparameter Defaults
P Gijsbers, F Pfisterer, JN van Rijn, B Bischl, J Vanschoren
arXiv preprint arXiv:2106.05767, 2021
2021
Kosten als Instrument zur Effizienzbeurteilung intensivmedizinischer Funktionseinheiten
T Maierhofer, F Pfisterer, A Bender, H Küchenhoff, O Mörer, H Burchardi, ...
Wiener klinisches Magazin 22 (2), 86-93, 2019
2019
Kosten als Instrument zur Effizienzbeurteilung intensivmedizinischer Funktionseinheiten
T Maierhofer, F Pfisterer, A Bender, H Küchenhoff, O Moerer, H Burchardi, ...
Medizinische Klinik-Intensivmedizin und Notfallmedizin 113 (7), 567-573, 2018
2018
Cost analysis as a tool for assessing the efficacy of intensive care units
T Maierhofer, F Pfisterer, A Bender, H Küchenhoff, O Moerer, H Burchardi, ...
Medizinische Klinik, Intensivmedizin und Notfallmedizin 113 (7), 567-573, 2017
2017
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