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Gruppiert nach: Dokumententyp |
Veröffentlichungsdatum
Anzahl der Publikationen: 3
2024
Schalk, Daniel; Bischl, Bernd 
ORCID: https://orcid.org/0000-0001-6002-6980 und Rügamer, David ORCID: https://orcid.org/0000-0002-8772-9202
(2024):
Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models.
In: Statistics and Computing, Bd. 34, Nr. 1, 31
[PDF, 835kB]
ORCID: https://orcid.org/0000-0001-6002-6980 und Rügamer, David ORCID: https://orcid.org/0000-0002-8772-9202
(2024):
Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models.
In: Statistics and Computing, Bd. 34, Nr. 1, 31
[PDF, 835kB]
2022
Schalk, Daniel; Bischl, Bernd und Ruegamer, David
(2022):
Accelerated Componentwise Gradient Boosting Using Efficient Data Representation and Momentum-Based Optimization.
In: Journal of Computational and Graphical Statistics, Bd. 32, Nr. 2: S. 631-641
2018
Schalk, Daniel
(2018):
Efficient and Distributed Model-Based Boosting for Large Datasets.
Masterarbeit,
Ludwig-Maximilians-Universität München
(Department of Statistics: Technical Reports) [PDF, 5MB]
(Department of Statistics: Technical Reports) [PDF, 5MB]