# Generative Models for Learning from Crowds

## TL;DR

This paper introduces generative probabilistic models for aggregating labels from crowds, utilizing Gibbs sampling and a new variational inference algorithm, demonstrating superior performance over existing methods.

## Contribution

The paper presents a novel generative modeling approach with a new inference algorithm for better label aggregation from crowds.

## Key findings

- Our methods outperform state-of-the-art label aggregation techniques.
- The proposed variational inference algorithm is efficient and effective.
- Empirical results validate the superiority of our models.

## Abstract

In this paper, we propose generative probabilistic models for label aggregation. We use Gibbs sampling and a novel variational inference algorithm to perform the posterior inference. Empirical results show that our methods consistently outperform state-of-the-art methods.