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  GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators

Chen, D., Orekondy, T., & Fritz, M. (2020). GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33 (pp. 12673-12684). Curran Associates, Inc.

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Genre: Conference Paper
Latex : {GS-WGAN}: {A} Gradient-Sanitized Approach for Learning Differentially Private Generators

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 Creators:
Chen, Dingfan1, Author           
Orekondy, Tribhuvanesh2, Author           
Fritz, Mario2, Author           
Affiliations:
1External Organizations, ou_persistent22              
2Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              

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Language(s): eng - English
 Dates: 2020
 Publication Status: Published online
 Pages: 12 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Chen_NeurIPS20
 Degree: -

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

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Title: Advances in Neural Information Processing Systems 33
  Abbreviation : NeurIPS 2020
  Other : 34th Conference on Neural Information Processing Systems
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, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Curran Associates, Inc.
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 12673 - 12684 Identifier: -