# Streaming Bayesian Inference for Crowdsourced Classification

arXiv: https://arxiv.org/abs/1911.05712 · HTML: https://mlsanity.com/paper/1911.05712/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

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

- [A Light-weight, Effective and Efficient Model for Label Aggregation in Crowdsourcing](https://mlsanity.com/paper/2212.00007/) (2022)
- [Stability of Weighted Majority Voting under Estimated Weights](https://mlsanity.com/paper/2207.06118/) (2022)
- [Towards Long-term Annotators: A Supervised Label Aggregation Baseline](https://mlsanity.com/paper/2311.14709/) (2023)

## Abstract (arXiv)

A key challenge in crowdsourcing is inferring the ground truth from noisy and unreliable data. To do so, existing approaches rely on collecting redundant information from the crowd, and aggregating it with some probabilistic method. However, oftentimes such methods are computationally inefficient, are restricted to some specific settings, or lack theoretical guarantees. In this paper, we revisit the problem of binary classification from crowdsourced data. Specifically we propose Streaming Bayesian Inference for Crowdsourcing (SBIC), a new algorithm that does not suffer from any of these limitations. First, SBIC has low complexity and can be used in a real-time online setting. Second, SBIC has the same accuracy as the best state-of-the-art algorithms in all settings. Third, SBIC has provable asymptotic guarantees both in the online and offline settings.