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Learning topic models -- provably and efficiently
Sanjeev Arora, Rong Ge, Yoni Halpern et al., 2018. Communications of the ACM.
A Communications of the ACM overview of anchor-word algorithms, which learn topic models with provable guarantees and run quickly in practice.
What it's used for here
Listed among the topic-model references in the inductive-methods survey.
Cited by on this site 1 record
1 methods surveys
SRCInductive methods
Cite it
Arora, S., Ge, R., Halpern, Y., Mimno, D., Moitra, A., Sontag, D., Wu, Y., & Zhu, M. (2018). Learning topic models -- provably and efficiently. Communications of the ACM. https://doi.org/10.1145/3186262
@article{arora2018learning,
author = {Arora, Sanjeev and Ge, Rong and Halpern, Yoni and Mimno, David and Moitra, Ankur and Sontag, David and Wu, Yichen and Zhu, Michael},
title = {Learning topic models -- provably and efficiently},
journal = {Communications of the ACM},
year = {2018},
doi = {10.1145/3186262},
url = {https://doi.org/10.1145/3186262}
}TY - JOUR TI - 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 JO - Communications of the ACM DO - 10.1145/3186262 UR - https://doi.org/10.1145/3186262 ER -
Arora, Sanjeev et al.. “Learning topic models -- provably and efficiently.” Communications of the ACM (2018). https://doi.org/10.1145/3186262.