| dc.contributor.author | Liu, Allen | |
| dc.contributor.author | Moitra, Ankur | |
| dc.date.accessioned | 2022-10-21T17:04:05Z | |
| dc.date.available | 2022-10-21T17:04:05Z | |
| dc.date.issued | 2021-06-15 | |
| dc.identifier.isbn | 978-1-4503-8053-9 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/145926 | |
| dc.publisher | ACM|Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing | en_US |
| dc.relation.isversionof | https://doi.org/10.1145/3406325.3451084 | en_US |
| dc.rights | Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. | en_US |
| dc.source | ACM|Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing | en_US |
| dc.title | Settling the Robust Learnability of Mixtures of Gaussians | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Liu, Allen and Moitra, Ankur. 2021. "Settling the Robust Learnability of Mixtures of Gaussians." | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
| dc.identifier.mitlicense | PUBLISHER_POLICY | |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/ConferencePaper | en_US |
| eprint.status | http://purl.org/eprint/status/NonPeerReviewed | en_US |
| dc.date.updated | 2022-10-20T14:16:14Z | |
| dc.language.rfc3066 | en | |
| dc.rights.holder | ACM | |
| dspace.date.submission | 2022-10-20T14:16:14Z | |
| mit.license | PUBLISHER_POLICY | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |
