# Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation

arXiv: https://arxiv.org/abs/1305.2452 · HTML: https://mlsanity.com/paper/1305.2452/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2013.

## Built on by (most cited first, 5 of 5)

- [Similarity of Classification Tasks](https://mlsanity.com/paper/2101.11201/) (2021)
- [Continual Learning with Dirichlet Generative-based Rehearsal](https://mlsanity.com/paper/2309.06917/) (2023)
- [Probabilistic task modelling for meta-learning](https://mlsanity.com/paper/2106.04802/) (2021)
- [ALBU: An approximate Loopy Belief message passing algorithm for LDA to improve performance on small data sets](https://mlsanity.com/paper/2110.00635/) (2021)
- [Fair Inference for Discrete Latent Variable Models](https://mlsanity.com/paper/2209.07044/) (2022)

## Abstract (arXiv)

In the internet era there has been an explosion in the amount of digital text information available, leading to difficulties of scale for traditional inference algorithms for topic models. Recent advances in stochastic variational inference algorithms for latent Dirichlet allocation (LDA) have made it feasible to learn topic models on large-scale corpora, but these methods do not currently take full advantage of the collapsed representation of the model. We propose a stochastic algorithm for collapsed variational Bayesian inference for LDA, which is simpler and more efficient than the state of the art method. We show connections between collapsed variational Bayesian inference and MAP estimation for LDA, and leverage these connections to prove convergence properties of the proposed algorithm. In experiments on large-scale text corpora, the algorithm was found to converge faster and often to a better solution than the previous method. Human-subject experiments also demonstrated that the method can learn coherent topics in seconds on small corpora, facilitating the use of topic models in interactive document analysis software.