Iterative Bayesian Learning for Crowdsourced Regression

TL;DR

This paper introduces an iterative Bayesian method for crowdsourced regression tasks that effectively learns worker quality and achieves optimal mean squared error, outperforming traditional averaging methods.

Contribution

The paper presents a novel Bayesian iterative algorithm specifically designed for crowdsourced regression, addressing the challenge of heterogeneous worker quality estimation.

Findings

The proposed method achieves optimal mean squared error.

Experimental results validate theoretical guarantees.

Outperforms simple averaging in real-world datasets.

Abstract

Crowdsourcing platforms emerged as popular venues for purchasing human intelligence at low cost for large volume of tasks. As many low-paid workers are prone to give noisy answers, a common practice is to add redundancy by assigning multiple workers to each task and then simply average out these answers. However, to fully harness the wisdom of the crowd, one needs to learn the heterogeneous quality of each worker. We resolve this fundamental challenge in crowdsourced regression tasks, i.e., the answer takes continuous labels, where identifying good or bad workers becomes much more non-trivial compared to a classification setting of discrete labels. In particular, we introduce a Bayesian iterative scheme and show that it provably achieves the optimal mean squared error. Our evaluations on synthetic and real-world datasets support our theoretical results and show the superiority of the proposed scheme.