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  Quasi-Newton Methods: A New Direction

Hennig, P., & Kiefel, M. (2012). Quasi-Newton Methods: A New Direction. In J. Langford, & J. Pineau (Eds.), Proceedings of the Twenty-Ninth International Conference on Machine Learning (pp. 25-32). Madison, WI: Omnipress. Retrieved from https://icml.cc/2012/papers.1.html.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000E-E907-7 Version Permalink: https://hdl.handle.net/21.11116/0000-000A-E05F-F
Genre: Conference Paper

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Locator:
https://doi.org/10.48550/arXiv.1206.4602 (Preprint)
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 Creators:
Hennig, Philipp1, Author           
Kiefel, Martin1, Author           
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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Free keywords: Abt. Schölkopf
 Abstract: -

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Language(s): eng - English
 Dates: 20122012
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: optimization
arXiv: 1206.4602
URI: https://icml.cc/2012/papers.1.html
 Degree: -

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Title: 29th International Conference on Machine Learning (ICML 2012)
Place of Event: Edinburgh
Start-/End Date: 2012-06-26 - 2012-07-01

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Title: Proceedings of the Twenty-Ninth International Conference on Machine Learning
  Other : Proceedings of the 29th International Conference on Machine Learning
Source Genre: Proceedings
 Creator(s):
Langford, John1, Editor
Pineau, Joelle1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Madison, WI : Omnipress
Pages: - Volume / Issue: 1 Sequence Number: - Start / End Page: 25 - 32 Identifier: ISBN: 978-1-4503-1285-1
URI: https://icml.cc/2012/papers.1.html