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  A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings

Park, J., & Muandet, K. (2021). A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings. In H. Larochelle, M. Ranzato, R. Hadsell, & M. F. Balcan (Eds.), Advances in Neural Information Processing Systems 33 (pp. 21247-21259). Red Hook, NY: Curran Associates, Inc. Retrieved from https://proceedings.neurips.cc/paper/2020/hash/f340f1b1f65b6df5b5e3f94d95b11daf-Abstract.html.

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 Creators:
Park, Junhyung1, Author           
Muandet, Krikamol1, 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: 20202021-07
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Degree: -

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Title: 34th Conference on Neural Information Processing Systems (NeurIPS 2020)
Place of Event: Online
Start-/End Date: 2020-12-06 - 2020-12-12

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Title: Advances in Neural Information Processing Systems 33
  Subtitle : 34th Conference on Neural Information Processing Systems (NeurIPS 2020)
Source Genre: Proceedings
 Creator(s):
Larochelle, H.1, Editor
Ranzato, M.1, Editor
Hadsell, R.1, Editor
Balcan, M. F. 1, Editor
Lin, H.1, Author
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Red Hook, NY : Curran Associates, Inc.
Pages: - Volume / Issue: 26 Sequence Number: - Start / End Page: 21247 - 21259 Identifier: ISBN: 9781713829546
URI: https://proceedings.neurips.cc/paper/2020