Learning topic models — Provably and efficiently

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

doi: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

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

Original language	English (US)
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

All Science Journal Classification (ASJC) codes

Computer Science(all)

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10.1145/3186262

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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",

year = "2018",

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language = "English (US)",

volume = "61",

pages = "85--93",

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T1 - Learning topic models — Provably and efficiently

AU - Arora, Sanjeev

AU - Ge, Rong

AU - Halpern, Yoni

AU - Mimno, David

AU - Moitra, Ankur

AU - Sontag, David

AU - Wu, Yichen

AU - Zhu, Michael

PY - 2018/4

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VL - 61

SP - 85

EP - 93

JO - Communications of the ACM

JF - Communications of the ACM

IS - 4

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Learning topic models — Provably and efficiently

All Science Journal Classification (ASJC) codes

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