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Eine Ebene nach oben |
Gruppiert nach: Dokumententyp |
Veröffentlichungsdatum
Anzahl der Publikationen: 10
2023
Bischl, Bernd 
ORCID: https://orcid.org/0000-0001-6002-6980; Binder, Martin; Lang, Michel ORCID: https://orcid.org/0000-0001-9754-0393; Pielok, Tobias; Richter, Jakob ORCID: https://orcid.org/0000-0003-4481-5554; Coors, Stefan ORCID: https://orcid.org/0000-0002-7465-2146; Thomas, Janek; Ullmann, Theresa ORCID: https://orcid.org/0000-0003-1215-8561; Becker, Marc ORCID: https://orcid.org/0000-0002-8115-0400; Boulesteix, Anne‐Laure ORCID: https://orcid.org/0000-0002-2729-0947; Deng, Difan und Lindauer, Marius ORCID: https://orcid.org/0000-0002-9675-3175
(2023):
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges.
In: WIREs Data Mining and Knowledge Discovery, Bd. 13, Nr. 2
[PDF, 6MB]
ORCID: https://orcid.org/0000-0001-6002-6980; Binder, Martin; Lang, Michel ORCID: https://orcid.org/0000-0001-9754-0393; Pielok, Tobias; Richter, Jakob ORCID: https://orcid.org/0000-0003-4481-5554; Coors, Stefan ORCID: https://orcid.org/0000-0002-7465-2146; Thomas, Janek; Ullmann, Theresa ORCID: https://orcid.org/0000-0003-1215-8561; Becker, Marc ORCID: https://orcid.org/0000-0002-8115-0400; Boulesteix, Anne‐Laure ORCID: https://orcid.org/0000-0002-2729-0947; Deng, Difan und Lindauer, Marius ORCID: https://orcid.org/0000-0002-9675-3175
(2023):
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges.
In: WIREs Data Mining and Knowledge Discovery, Bd. 13, Nr. 2
[PDF, 6MB]
2022
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]
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
2021
Goschenhofer, Jann; Hvingelby, Rasmus; Rügamer, David ORCID: https://orcid.org/0000-0002-8772-9202; Thomas, Janek; Wagner, Moritz und Bischl, Bernd
(2021):
Deep Semi-supervised Learning for Time Series Classification.
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), Pasadena, CA, USA, 13-16 December 2021.
In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA),
New York: IEEE. S. 422-428
ORCID: https://orcid.org/0000-0002-8772-9202; Thomas, Janek; Wagner, Moritz und Bischl, Bernd
(2021):
Deep Semi-supervised Learning for Time Series Classification.
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), Pasadena, CA, USA, 13-16 December 2021.
In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA),
New York: IEEE. S. 422-428
2020
Binder, Martin; Moosbauer, Julia; Thomas, Janek und Bischl, Bernd
(2020):
Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles.
In: Gecco'20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference: S. 471-479
2018
Thomas, Janek; Mayr, Andreas; Bischl, Bernd; Schmid, Matthias; Smith, Adam und Hofner, Benjamin
(2018):
Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates.
In: Statistics and Computing, Bd. 28, Nr. 3: S. 673-687
2017
Thomas, Janek; Hepp, Tobias; Mayr, Andreas und Bischl, Bernd
(2017):
Probing for Sparse and Fast Variable Selection with Model-Based Boosting.
In: Computational and Mathematical Methods in Medicine, Bd. 2017, 1421409
[PDF, 1MB]
2016
Thomas, Janek
(22. Februar 2016):
Stability selection for component-wise gradient boosting in multiple dimensions.
Masterarbeit,
Ludwig-Maximilians-Universität München
[PDF, 2MB]
[PDF, 2MB]
Rietzler, Michael; Geiselhart, Florian; Thomas, Janek und Rukzio, Enrico
(2016):
FusionKit: a generic toolkit for skeleton, marker and rigid-body tracking.
8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, Brüssel, 21.-24. Juni 2016.
In: Proceedings of the 8th ACM SIGCHI Symposium on Engineering Interactive Computing Systems,
S. 73-84
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