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dc.contributor.authorAnagnostides, Ioannis
dc.contributor.authorDaskalakis, Constantinos
dc.contributor.authorFarina, Gabriele
dc.contributor.authorFishelson, Maxwell
dc.contributor.authorGolowich, Noah
dc.contributor.authorSandholm, Tuomas
dc.date.accessioned2022-11-15T14:01:11Z
dc.date.available2022-11-15T14:01:11Z
dc.date.issued2022-06-09
dc.identifier.isbn978-1-4503-9264-8
dc.identifier.urihttps://hdl.handle.net/1721.1/146430
dc.publisherACM|Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computingen_US
dc.relation.isversionofhttps://doi.org/10.1145/3519935.3520031en_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.sourceACM|Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computingen_US
dc.titleNear-Optimal No-Regret Learning for Correlated Equilibria in Multi-player General-Sum Gamesen_US
dc.typeArticleen_US
dc.identifier.citationAnagnostides, Ioannis, Daskalakis, Constantinos, Farina, Gabriele, Fishelson, Maxwell, Golowich, Noah et al. 2022. "Near-Optimal No-Regret Learning for Correlated Equilibria in Multi-player General-Sum Games."
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.identifier.mitlicensePUBLISHER_POLICY
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.updated2022-11-03T12:13:19Z
dc.language.rfc3066en
dc.rights.holderACM
dspace.date.submission2022-11-03T12:13:19Z
mit.licensePUBLISHER_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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