# Learning Supervised Topic Models for Classification and Regression from Crowds

arXiv: https://arxiv.org/abs/1808.05902 · HTML: https://mlsanity.com/paper/1808.05902/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2018.

## Built on by (most cited first, 16 of 16)

- [Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning](https://mlsanity.com/paper/2104.08676/) (2021)
- [To Aggregate or Not? Learning with Separate Noisy Labels](https://mlsanity.com/paper/2206.07181/) (2022)
- [Trustable Co-label Learning from Multiple Noisy Annotators](https://mlsanity.com/paper/2203.04199/) (2022)
- [Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization](https://mlsanity.com/paper/2306.03288/) (2023)
- [Multi-annotator Deep Learning: A Probabilistic Framework for Classification](https://mlsanity.com/paper/2304.02539/) (2023)
- [Learning to Complement and to Defer to Multiple Users](https://mlsanity.com/paper/2407.07003/) (2024)
- [Learning from Noisy Labels via Conditional Distributionally Robust Optimization](https://mlsanity.com/paper/2411.17113/) (2024)
- [Learning to Complement with Multiple Humans](https://mlsanity.com/paper/2311.13172/) (2023)
- [Learning from Multiple Annotators by Incorporating Instance Features](https://mlsanity.com/paper/2106.15146/) (2021)
- [A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement](https://mlsanity.com/paper/2404.04035/) (2024)
- [Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension](https://mlsanity.com/paper/2405.03386/) (2024)
- [Weakly Supervised Prototype Topic Model with Discriminative Seed Words: Modifying the Category Prior by Self-exploring Supervised Signals](https://mlsanity.com/paper/2112.03009/) (2021)
- [Expertise-based Weighting for Regression Models with Noisy Labels](https://mlsanity.com/paper/2305.07430/) (2023)
- [Label Selection Approach to Learning from Crowds](https://mlsanity.com/paper/2308.10396/) (2023)
- [crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels](https://mlsanity.com/paper/2504.09085/) (2025)
- [Dual-Primal Graph VAEs for Noisy Label Aggregation](https://mlsanity.com/paper/2608.11473/) (2026)

## Abstract (arXiv)

The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.