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Florian Pfisterer
Florian Pfisterer
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
1082019
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
262021
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, ...
142019
mlr3pipelines-Flexible Machine Learning Pipelines in R.
M Binder, F Pfisterer, M Lang, L Schneider, L Kotthoff, B Bischl
J. Mach. Learn. Res. 22, 184:1-184:7, 2021
122021
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features
F Pargent, F Pfisterer, J Thomas, B Bischl
Computational Statistics, 1-22, 2022
92022
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
92020
YAHPO Gym-An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization
F Pfisterer, L Schneider, J Moosbauer, M Binder, B Bischl
First Conference on Automated Machine Learning (Main Track), 2022
8*2022
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
82019
mlr Tutorial
J Schiffner, B Bischl, M Lang, J Richter, ZM Jones, P Probst, F Pfisterer, ...
arXiv preprint arXiv:1609.06146, 2016
82016
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
72019
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
72018
Multi-objective automatic machine learning with autoxgboostmc
F Pfisterer, S Coors, J Thomas, B Bischl
arXiv preprint arXiv:1908.10796, 2019
62019
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
52021
Neural mixture distributional regression
D Rügamer, F Pfisterer, B Bischl
arXiv preprint arXiv:2010.06889, 2020
52020
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
52020
Deepregression: a flexible neural network framework for semi-structured deep distributional regression
D Rügamer, C Kolb, C Fritz, F Pfisterer, B Bischl, R Shen, C Bukas, ...
arXiv preprint arXiv:2104.02705, 2021
42021
mlr3 book
M Becker, M Binder, B Bischl, M Lang, F Pfisterer, NG Reich, J Richter, ...
URl: https://mlr3book. mlr-org. com, 2020
42020
Towards human centered AutoML
F Pfisterer, J Thomas, B Bischl
arXiv preprint arXiv:1911.02391, 2019
42019
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
32021
Multi-Objective Hyperparameter Optimization--An Overview
F Karl, T Pielok, J Moosbauer, F Pfisterer, S Coors, M Binder, L Schneider, ...
arXiv preprint arXiv:2206.07438, 2022
12022
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