# Prediction Focused Topic Models via Feature Selection

arXiv: https://arxiv.org/abs/1910.05495 · HTML: https://mlsanity.com/paper/1910.05495/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2019.

## Built on by (most cited first, 3 of 3)

- [On Learning Prediction-Focused Mixtures](https://mlsanity.com/paper/2110.13221/) (2021)
- [A Joint Learning Approach for Semi-supervised Neural Topic Modeling](https://mlsanity.com/paper/2204.03208/) (2022)
- [DePICT: Decision-Preserving Interface for Constrained Downstream Tasks](https://mlsanity.com/paper/2610.03945/) (2026)

## Abstract (arXiv)

Supervised topic models are often sought to balance prediction quality and interpretability. However, when models are (inevitably) misspecified, standard approaches rarely deliver on both. We introduce a novel approach, the prediction-focused topic model, that uses the supervisory signal to retain only vocabulary terms that improve, or at least do not hinder, prediction performance. By removing terms with irrelevant signal, the topic model is able to learn task-relevant, coherent topics. We demonstrate on several data sets that compared to existing approaches, prediction-focused topic models learn much more coherent topics while maintaining competitive predictions.