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Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters
| dc.contributor.author | Uhler, Caroline | |
| dc.date.accessioned | 2021-11-03T14:29:09Z | |
| dc.date.available | 2021-11-03T14:29:09Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/137192 | |
| dc.language.iso | en | |
| dc.relation.isversionof | http://proceedings.mlr.press/v108/wang20g/wang20g.pdf | 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 | Proceedings of Machine Learning Research | en_US |
| dc.title | Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Uhler, Caroline. 2020. "Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters." INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108, 108. | |
| dc.relation.journal | INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 108 | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dc.date.updated | 2021-04-07T12:17:16Z | |
| dspace.orderedauthors | Wang, Y; Roy, U; Uhler, C | en_US |
| dspace.date.submission | 2021-04-07T12:17:17Z | |
| mit.journal.volume | 108 | en_US |
| mit.license | PUBLISHER_POLICY | |
| mit.metadata.status | Authority Work and Publication Information Needed | en_US |
