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  A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control

Zhu, J.-J., Diehl, M., & Schölkopf, B. (2020). A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control. In A. M. Bayen, A. Jadbabaie, G. Pappas, P. A. Parrilo, B. Recht, C. Tomlin, et al. (Eds.), Proceedings of the 2nd Conference on Learning for Dynamics and Control (pp. 915-923). PMLR. Retrieved from http://proceedings.mlr.press/v120/zhu20a.html.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000B-0A86-3 Version Permalink: https://hdl.handle.net/21.11116/0000-000B-0A87-2
Genre: Conference Paper

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Locator:
http://proceedings.mlr.press/v120/zhu20a.html (Publisher version)
Description:
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OA-Status:
Gold
Locator:
https://doi.org/10.48550/arXiv.2001.10398 (Preprint)
Description:
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OA-Status:
Green

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 Creators:
Zhu, Jia-Jie1, Author           
Diehl, Moritz2, Author
Schölkopf, Bernhard1, Author           
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              
2External Organizations, ou_persistent22              

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Free keywords: Abt. Schölkopf
 Abstract: -

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Language(s): eng - English
 Dates: 2020
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: ZhuDieSch20
URI: http://proceedings.mlr.press/v120/zhu20a.html
 Degree: -

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Title: 2nd Conference on Learning for Dynamics and Control (L4DC)
Place of Event: Online
Start-/End Date: 2020-06-11 - 2020-06-12

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Source 1

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Title: Proceedings of the 2nd Conference on Learning for Dynamics and Control
Source Genre: Proceedings
 Creator(s):
Bayen, Alexandre M.1, Editor
Jadbabaie, Ali1, Editor
Pappas, George1, Editor
Parrilo, Pablo A.1, Editor
Recht, Benjamin1, Editor
Tomlin, Claire1, Editor
Zeilinger, Melanie1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: PMLR
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 915 - 923 Identifier: URI: https://proceedings.mlr.press/v120/

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Title: Proceedings of Machine Learning Research (PMLR)
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: PMLR
Pages: - Volume / Issue: 120 Sequence Number: - Start / End Page: - Identifier: ISSN: 2640-3498