Skip to content
DraconiticResearch
Search Sources API
189,105 studies31 sourcesUpdated 13 min ago
Peer-reviewedLicence not statedStatistical Science

Evaluating Machine Learning Models in Nonstandard Settings: An Overview and New Findings

  • Roman Hornung1
  • Malte Nalenz2
  • Lennart Schneider3
  • Andreas Bender4
  • Ludwig Bothmann5
  • Florian Dumpert6
  • Bernd Bischl7
  • Thomas Augustin8
  • Anne‐Laure Boulesteix9
9 affiliations
  1. 1Roman Hornung is Senior Research Fellow, LMU Munich, Faculty of Medicine, Institute for Medical Information Processing, Biometry and Epidemiology, Munich Center for Machine Learning (MCML), Munich, Germany
  2. 2Malte Nalenz was Senior Research Fellow at the LMU Munich, Department of Statistics, Munich, Germany, while doing this work
  3. 3Lennart Schneider is Research Fellow, LMU Munich, Department of Statistics, Munich Center for Machine Learning (MCML), Munich, Germany
  4. 4Andreas Bender is Senior Research Fellow, LMU Munich, Department of Statistics, Munich Center for Machine Learning (MCML), Munich, Germany
  5. 5Ludwig Bothmann is Senior Research Fellow, LMU Munich, Department of Statistics, Munich Center for Machine Learning (MCML), Munich, Germany
  6. 6Florian Dumpert is head of the Section for Artificial Intelligence and Big Data, Federal Statistical Office of Germany, Wiesbaden, Germany
  7. 7Bernd Bischl is Professor, LMU Munich, Department of Statistics, Munich Center for Machine Learning (MCML), Munich, Germany
  8. 8Thomas Augustin is Professor, LMU Munich, Department of Statistics, Munich, Germany
  9. 9Anne-Laure Boulesteix is Professor, LMU Munich, Faculty of Medicine, Institute for Medical Information Processing, Biometry and Epidemiology, Munich Center for Machine Learning (MCML), Munich, Germany
Open at sourcePDFDOICite

Related

  1. Directional Statistics in Signal Processing: A Geometry Driven Overview of Models, Learning, and Applications
    Anders S. Olsen, Kanti V. Mardia, Dimitri Van De Ville +1
    arXivPreprintCC BY-NC-ND 4.0Full textStatisticsarXiv
  2. An overview of machine learning-enhanced iterative methods for systems of linear and nonlinear equations
    Yuhuang Meng, Jing Zhao, Alexander Heinlein
    arXivPreprintarXiv licenceFull textNumerical analysis & scientific computingarXiv
  3. Nonstandard combinatorial ultrafilters and a new local witness model
    Johannes Heil, Moritz Müller
    arXivPreprintCC BY-NC-SA 4.0Full textLogic & foundationsarXiv
  4. Bifurcation phenomena in one-phase quasilinear equations with nonstandard growth
    Junior da Silva Bessa, Alan Pio Sousa
    arXivPreprintarXiv licenceFull textAnalysis & PDEsarXiv
  5. A nonstandard selfinjective algebra of Euclidean type
    Hideto Asashiba
    arXivPreprintCC BY-NC-ND 4.0Full textAlgebra & number theoryarXiv

Cite

BibTeXRIS
BibTeX
@article{hornung2026evaluating,
  title = {{Evaluating Machine Learning Models in Nonstandard Settings: An Overview and New Findings}},
  author = {Roman Hornung and Malte Nalenz and Lennart Schneider and Andreas Bender and Ludwig Bothmann and Florian Dumpert and Bernd Bischl and Thomas Augustin and Anne‐Laure Boulesteix},
  journal = {Statistical Science},
  year = {2026},
  month = jul,
  doi = {10.1214/25-sts984},
  url = {http://arxiv.org/abs/2310.15108},
}
RIS
TY  - JOUR
TI  - Evaluating Machine Learning Models in Nonstandard Settings: An Overview and New Findings
AU  - Roman Hornung
AU  - Malte Nalenz
AU  - Lennart Schneider
AU  - Andreas Bender
AU  - Ludwig Bothmann
AU  - Florian Dumpert
AU  - Bernd Bischl
AU  - Thomas Augustin
AU  - Anne‐Laure Boulesteix
PY  - 2026
DA  - 2026/07/31
JO  - Statistical Science
DO  - 10.1214/25-sts984
UR  - http://arxiv.org/abs/2310.15108
SN  - 0883-4237
SN  - 2168-8745
ER  -