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A Practical Algorithm for Topic Modeling with Provable Guarantees

Author(s): Arora, Sanjeev; Ge, Rong; Halpern, Yonatan; Mimno, David; Moitra, Ankur; et al

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dc.contributor.authorArora, Sanjeev-
dc.contributor.authorGe, Rong-
dc.contributor.authorHalpern, Yonatan-
dc.contributor.authorMimno, David-
dc.contributor.authorMoitra, Ankur-
dc.contributor.authorSontag, David-
dc.contributor.authorWu, Yichen-
dc.contributor.authorZhu, Michael-
dc.date.accessioned2021-10-08T19:51:07Z-
dc.date.available2021-10-08T19:51:07Z-
dc.date.issued2013en_US
dc.identifier.citationArora, Sanjeev, Rong Ge, Yonatan Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, and Michael Zhu. "A Practical Algorithm for Topic Modeling with Provable Guarantees." In Proceedings of the 30th International Conference on Machine Learning (2013): pp. 280-288.en_US
dc.identifier.issn2640-3498-
dc.identifier.urihttp://proceedings.mlr.press/v28/arora13.pdf-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1h27j-
dc.description.abstractTopic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora. Most approaches to topic model learning have been based on a maximum likelihood objective. Efficient algorithms exist that attempt to approximate this objective, but they have no provable guarantees. Recently, algorithms have been introduced that provide provable bounds, but these algorithms are not practical because they are inefficient and not robust to violations of model assumptions. In this paper we present an algorithm for learning topic models that is both provable and practical. The algorithm produces results comparable to the best MCMC implementations while running orders of magnitude faster.en_US
dc.format.extent280 - 288en_US
dc.language.isoen_USen_US
dc.relation.ispartofProceedings of the 30th International Conference on Machine Learningen_US
dc.rightsFinal published version. Article is made available in OAR by the publisher's permission or policy.en_US
dc.titleA Practical Algorithm for Topic Modeling with Provable Guaranteesen_US
dc.typeConference Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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