![[höher]](/style/images/multi_up.png)
Eine Ebene höherGruppiert nach:
Dokumententyp
| Veröffentlichungsdatum
Springe zu: Zeitschriftenartikel | Konferenzbeitrag
Anzahl der Publikationen: 9
Zeitschriftenartikel
Herrmann, Moritz 
ORCID: https://orcid.org/0000-0002-4893-5812; Pfisterer, Florian und Scheipl, Fabian
(2023):
A geometric framework for outlier detection in high‐dimensional data.
In: WIREs Data Mining and Knowledge Discovery, Bd. 13, Nr. 3
[PDF, 43MB]
ORCID: https://orcid.org/0000-0002-4893-5812; Pfisterer, Florian und Scheipl, Fabian
(2023):
A geometric framework for outlier detection in high‐dimensional data.
In: WIREs Data Mining and Knowledge Discovery, Bd. 13, Nr. 3
[PDF, 43MB]
Rügamer, David ORCID: https://orcid.org/0000-0002-8772-9202; Pfisterer, Florian; Bischl, Bernd ORCID: https://orcid.org/0000-0001-6002-6980 und Grün, Bettina
(2023):
Mixture of experts distributional regression: implementation using robust estimation with adaptive first-order methods.
In: AStA Advances in Statistical Analysis [Forthcoming]
ORCID: https://orcid.org/0000-0002-8772-9202; Pfisterer, Florian; Bischl, Bernd ORCID: https://orcid.org/0000-0001-6002-6980 und Grün, Bettina
(2023):
Mixture of experts distributional regression: implementation using robust estimation with adaptive first-order methods.
In: AStA Advances in Statistical Analysis [Forthcoming]
Pargent, Florian ORCID: https://orcid.org/0000-0002-2388-553X; Pfisterer, Florian; Thomas, Janek und Bischl, Bernd ORCID: https://orcid.org/0000-0001-6002-6980
(2022):
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features.
In: Computational Statistics, Bd. 37, Nr. 5: S. 2671-2692
[PDF, 697kB]
ORCID: https://orcid.org/0000-0002-2388-553X; Pfisterer, Florian; Thomas, Janek und Bischl, Bernd ORCID: https://orcid.org/0000-0001-6002-6980
(2022):
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features.
In: Computational Statistics, Bd. 37, Nr. 5: S. 2671-2692
[PDF, 697kB]
Moosbauer, Julia; Binder, Martin; Schneider, Lennart; Pfisterer, Florian; Becker, Marc; Lang, Michel; Kotthoff, Lars und Bischl, Bernd
(2022):
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers.
In: IEEE Transactions on Evolutionary Computation (T-Evc), Bd. 26, Nr. 6: S. 1336-1350
Binder, Martin; Pfisterer, Florian; Lang, Michel; Schneider, Lennart; Kotthoff, Lars und Bischl, Bernd
(2021):
mlr3pipelines-Flexible Machine Learning Pipelines in R.
In: Journal of Machine Learning Research, Bd. 22
Sun, Xudong; Bommert, Andrea; Pfisterer, Florian; Rähenfürher, Jörg; Lang, Michel und Bischl, Bernd
(2020):
High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions.
In: Intelligent Systems and Applications, Vol 1, Bd. 1037: S. 629-647
Schiffner, Julia; Bischl, Bernd; Lang, Michel; Richter, Jakob; Jones, Zachary M.; Probst, Philipp; Pfisterer, Florian; Gallo, Mason; Kirchhoff, Dominik; Kühn, Tobias; Thomas, Janek und Kotthoff, Lars
(2016):
mlr Tutorial.
In: CoRR, Bd. abs/1609.06146
Konferenzbeitrag
Dandl, Susanne; Pfisterer, Florian und Bischl, Bernd ORCID: https://orcid.org/0000-0001-6002-6980
(2022):
Multi-objective counterfactual fairness.
GECCO '22, Genetic and Evolutionary Computation Conference, Boston Massachusetts, July 9 - 13, 2022.
In: Proceedings of the Genetic and Evolutionary Computation Conference Companion,
New York: Association for Computing Machinery. S. 328-331
ORCID: https://orcid.org/0000-0001-6002-6980
(2022):
Multi-objective counterfactual fairness.
GECCO '22, Genetic and Evolutionary Computation Conference, Boston Massachusetts, July 9 - 13, 2022.
In: Proceedings of the Genetic and Evolutionary Computation Conference Companion,
New York: Association for Computing Machinery. S. 328-331
Schneider, Lennart; Pfisterer, Florian; Thomas, Janek und Bischl, Bernd ORCID: https://orcid.org/0000-0001-6002-6980
(2022):
A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models.
GECCO '22: Genetic and Evolutionary Computation Conference, Boston Massachusetts (hybrid), July 9-13, 2022.
In: Proceedings of the Genetic and Evolutionary Computation Conference Companion,
Association of Computing Machinery: New York. S. 2136-2142
ORCID: https://orcid.org/0000-0001-6002-6980
(2022):
A collection of quality diversity optimization problems derived from hyperparameter optimization of machine learning models.
GECCO '22: Genetic and Evolutionary Computation Conference, Boston Massachusetts (hybrid), July 9-13, 2022.
In: Proceedings of the Genetic and Evolutionary Computation Conference Companion,
Association of Computing Machinery: New York. S. 2136-2142