MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Continuous LWE is as Hard as LWE & Applications to Learning Gaussian Mixtures

Author(s)
Gupte, Aparna; Vafa, Neekon; Vaikuntanathan, Vinod
Thumbnail
DownloadCLWE-gaussian-hardness.pdf (657.8Kb)
Open Access Policy

Open Access Policy

Creative Commons Attribution-Noncommercial-Share Alike

Terms of use
Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
Metadata
Show full item record
Date issued
2022-10
URI
https://hdl.handle.net/1721.1/148100
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)
Publisher
IEEE
Citation
Gupte, Aparna, Vafa, Neekon and Vaikuntanathan, Vinod. 2022. "Continuous LWE is as Hard as LWE & Applications to Learning Gaussian Mixtures." 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS).
Version: Author's final manuscript

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.