Scalable Training of Hierarchical Topic Models

Summary: Scalable hLDA with partially collapsed Gibbs sampling and tree initialization to mitigate local optima in hierarchical topic models. Vectorized layouts and distributed dynamic matrices/trees yield 87x speedup vs prior hLDA, scalable to many cores. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 11975
Venue: VLDB
Year: 2018
Pagerank: -
Overall Rank: 13,529 | 7.18%
DOI: 10.14778/3192965.3192972

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Authors

1. Jianfei Chen (Tsinghua University)
2. Jun Zhu (Tsinghua University)
3. Jie Lu (Tsinghua University)
4. Shixia Liu (Tsinghua University)

BibTeX Citation

@article{chen_vldb18,
        title = {{Scalable Training of Hierarchical Topic Models}},
        author = {Chen, Jianfei and Zhu, Jun and Lu, Jie and Liu, Shixia},
        journal = {PVLDB},
        series = {{VLDB} '18},
        volume = {11},
        number = {7},
        pages = {826--839},
        doi = {10.14778/3192965.3192972},
        url = {https://doi.org/10.14778/3192965.3192972},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
6,969	WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation	2016	VLDB	5.7303405e-05

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