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dc.contributor.authorDagan, Yuval
dc.contributor.authorDaskalakis, Constantinos
dc.contributor.authorDikkala, Nishanth
dc.contributor.authorKandiros, Anthimos Vardis
dc.date.accessioned2022-10-27T18:48:50Z
dc.date.available2022-06-17T16:15:43Z
dc.date.available2022-10-27T18:48:50Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/143465.2
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionof10.1145/3406325.3451074en_US
dc.rightsArticle 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.sourceACMen_US
dc.titleLearning Ising models from one or multiple samplesen_US
dc.typeArticleen_US
dc.identifier.citationDagan, Yuval, Daskalakis, Constantinos, Dikkala, Nishanth and Kandiros, Anthimos Vardis. 2021. "Learning Ising models from one or multiple samples." Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.relation.journalProceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computingen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-06-17T16:11:09Z
dspace.orderedauthorsDagan, Y; Daskalakis, C; Dikkala, N; Kandiros, AVen_US
dspace.date.submission2022-06-17T16:11:11Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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