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On kernel-based statistical learning in the mean field limit
; ;
2023
Online
DOI: 10.48550/arXiv.2310.18074
Einrichtungen
- Lehrstuhl für Data Science im Maschinenbau (422610)
- Lehrstuhl für Numerische Mathematik (114610)
- Fachgruppe Informatik (120000)
- Fachgruppe Mathematik (110000)
Projekte
- SFDdM035 - Novel approaches for machine learning for multi particle system interactions (EXS-SF-SFDdM035) (EXS-SF-SFDdM035)
- ERS Seed Fund - Exploratory Research Space: Seed Fund (2) als Anschubfinanzierung zur Erforschung neuer interdisziplinärer Ideen (EXS-SF) (EXS-SF)
- EXS - Excellence Strategy (EXS) (EXS)
Inhaltliche Beschreibung (Schlagwörter)
FOS: Computer and information sciences (Genormte SW) ; FOS: Mathematics (Genormte SW) ; Machine Learning (cs.LG) (Genormte SW) ; Machine Learning (stat.ML) (Genormte SW) ; Statistics Theory (math.ST) (Genormte SW)
External link: 
Fulltext by arXiv.org
Dokumenttyp
Preprint
Format
online
Sprache
English
Externe Identnummern
arXiv: arXiv:2310.18074
Interne Identnummern
RWTH-2023-11022
Datensatz-ID: 973845
Beteiligte Länder
Germany
Related:
Contribution to a book/Contribution to a conference proceedings
On kernel-based statistical learning in the mean field limit
Advances in Neural Information Processing Systems 36 (NeurIPS 2023) / edited by: A. Oh and T. Naumann and A. Globerson and K. Saenko and M. Hardt and S. Levine
37. Conference on Neural Information Processing Systems, NeurIPS 2023, New Orleans, LANew Orleans, LA, USA, 10 Dec 2023 - 16 Dec 2023
[Erscheinungsort nicht ermittelbar] : [Verlag nicht ermittelbar], Advances in neural information processing systems 36, [1]-28 (2023)
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