# Improving the Inference of Topic Models via Infinite Latent State Replications

arXiv: https://arxiv.org/abs/2301.12974 · HTML: https://mlsanity.com/paper/2301.12974/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 5 of 5)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Poisson Random Fields for Dynamic Feature Models](https://mlsanity.com/paper/1611.07460/) (2016)
- [LightLDA: Big Topic Models on Modest Compute Clusters](https://mlsanity.com/paper/1412.1576/) (2014)
- [WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation](https://mlsanity.com/paper/1510.08628/) (2015)
- [SAME but Different: Fast and High-Quality Gibbs Parameter Estimation](https://mlsanity.com/paper/1409.5402/) (2014)

## Abstract (arXiv)

In text mining, topic models are a type of probabilistic generative models for inferring latent semantic topics from text corpus. One of the most popular inference approaches to topic models is perhaps collapsed Gibbs sampling (CGS), which typically samples one single topic label for each observed document-word pair. In this paper, we aim at improving the inference of CGS for topic models. We propose to leverage state augmentation technique by maximizing the number of topic samples to infinity, and then develop a new inference approach, called infinite latent state replication (ILR), to generate robust soft topic assignment for each given document-word pair. Experimental results on the publicly available datasets show that ILR outperforms CGS for inference of existing established topic models.