Learning topic models — Provably and efficiently

Sanjeev Arora; Rong Ge; Yoni Halpern; David Mimno; Ankur Moitra; David Sontag; Yichen Wu; Michael Zhu

doi:https://doi.org/10.1145/3186262

Learning topic models — Provably and efficiently

Sanjeev Arora, Rong Ge, Yoni Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, Michael Zhu

Princeton University

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Original language	American English
Pages (from-to)	85-93
Number of pages	9
Journal	Communications of the ACM
Volume	61
Issue number	4
DOIs	https://doi.org/10.1145/3186262
State	Published - Apr 2018

ASJC Scopus subject areas

Computer Science(all)

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https://doi.org/10.1145/3186262

Cite this

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title = "Learning topic models — Provably and efficiently",

author = "Sanjeev Arora and Rong Ge and Yoni Halpern and David Mimno and Ankur Moitra and David Sontag and Yichen Wu and Michael Zhu",

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AU - Arora, Sanjeev

AU - Ge, Rong

AU - Halpern, Yoni

AU - Mimno, David

AU - Moitra, Ankur

AU - Sontag, David

AU - Wu, Yichen

AU - Zhu, Michael

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SP - 85

EP - 93

JO - Communications of the ACM

JF - Communications of the ACM

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Learning topic models — Provably and efficiently

ASJC Scopus subject areas

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