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: 11787
Venue: VLDB
Year: 2018
Pagerank: -
Overall Rank: 13,328 | 7.28%
DOI: 10.14778/3192965.3192972

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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
6,014	WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation	2016	VLDB	5.2415551e-05

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