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    Area of Science:

    • Machine Learning
    • Data Science
    • Artificial Intelligence

    Background:

    • Crowdsourcing relies on aggregating noisy labels from multiple workers.
    • Existing methods like majority voting have limitations in handling label noise and worker reliability.

    Purpose of the Study:

    • To develop advanced aggregation strategies for accurate label inference in crowdsourcing.
    • To improve the discriminative power of majority voting and incorporate generative modeling flexibility.

    Main Methods:

    • Introduced max-margin majority voting (M$^3$3V) to enhance majority voting.
    • Formulated joint learning as a regularized Bayesian inference (RegBayes) problem.
    • Developed a Bayesian model generalizing Dawid-Skene and M$^3$3V, handling worker confusion matrices and ordinal label structures.

    Main Results:

    • The proposed Bayesian model naturally incorporates existing estimators like Dawid-Skene and M$^3$3V.
    • The model was extended to effectively handle crowdsourced labels with ordinal structures.
    • An online learning setting for streaming labels was also addressed.

    Conclusions:

    • The novel methods demonstrate competitive performance, often surpassing state-of-the-art estimators.
    • The Bayesian framework offers flexibility for various crowdsourcing application settings and data structures.