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arXiv · 1606.09632

A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness

Nihar B. Shah·Sivaraman Balakrishnan·Martin J. Wainwright
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Abstract

The task of aggregating and denoising crowd-labeled data has gained increased significance with the advent of crowdsourcing platforms and massive datasets. We propose a permutation-based model for crowd labeled data that is a significant generalization of the classical Dawid-Skene model, and introduce a new error metric by which to compare different estimators. We derive global minimax rates for the permutation-based model that are sharp up to logarithmic factors, and match the minimax lower bounds derived under the simpler Dawid-Skene model. We then design two computationally-efficient estimators: the WAN estimator for the setting where the ordering of workers in terms of their abilities is approximately known, and the OBI-WAN estimator where that is not known. For each of these estimators, we provide non-asymptotic bounds on their performance. We conduct synthetic simulations and experiments on real-world crowdsourcing data, and the experimental results corroborate our theoretical findings.

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BibTeXRIS

Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright. 2021-01-10. A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness. https://doi.org/10.1109/tit.2020.3045613

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