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

DOI10.1109/TIT.2020.3045613zbMath1475.62170arXiv1606.09632OpenAlexW2473938289WikidataQ114373364 ScholiaQ114373364MaRDI QIDQ5001782

Nihar B. Shah, Sivaraman Balakrishnan, Martin J. Wainwright

Publication date: 23 July 2021

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1606.09632

zbMATH Keywords

global minimax ratespermutation-based model for crowd labeled data

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Statistical aspects of big data and data science (62R07)

Related Items (5)

Isotonic regression with unknown permutations: statistics, computation and adaptation ⋮ Low Permutation-rank Matrices: Structural Properties and Noisy Completion ⋮ Optimal permutation estimation in crowdsourcing problems ⋮ Optimal detection of the feature matching map in presence of noise and outliers ⋮ Doubly Robust Crowdsourcing

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