Learning-from-crowds aims to design proper aggregation strategies to infer the unknown true labels from the noisy labels provided by ordinary web workers. This paper presents max-margin majority voting (M$^3$3V) to improve the discriminative ability of majority voting and further presents a Bayesian generalization to incorporate the flexibility of generative methods on modeling noisy observations with worker confusion matrices for different application settings. We first introduce the crowdsourcing margin of majority voting, then we formulate the joint learning as a regularized Bayesian inference (RegBayes) problem, where the posterior regularization is derived by maximizing the margin between the aggregated score of a potential true label and that of any alternative label. Our Bayesian model naturally covers the Dawid-Skene estimator and M$^3$3V as its two special cases. Due to the flexibility of our model, we extend it to handle crowdsourced labels with an ordinal structure with the main ideas about the crowdsourcing margin unchanged. Moreover, we consider an online learning-from-crowds setting where labels coming in a stream. Empirical results demonstrate that our methods are competitive, often achieving better results than state-of-the-art estimators.

中文翻译：

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最大保证金多数投票赞成向人群学习

从人群中学习旨在设计适当的汇总策略，以从普通网络工作者提供的嘈杂标签中推断出未知的真实标签。本文提出了最大边际多数投票（M $ ^ 3 $ 3V）以提高多数投票的判别能力，并进一步提出了一种贝叶斯归纳，将生成方法的灵活性与工人混淆矩阵在不同应用场合下的噪声观测建模相结合。我们首先介绍多数投票的众包利润率，然后将联合学习公式化为正则贝叶斯推理（RegBayes）问题，其中后验正则化是通过最大化潜在真实标签的总得分与任何替代方案的总得分之间的利润来得出的标签。我们的贝叶斯模型自然涵盖了Dawid-Skene估计量和M $ ^ 3 $ 3V这两个特例。由于我们模型的灵活性，我们将其扩展为处理具有序数结构的众包标签，而有关众包利润率的主要思想保持不变。此外，我们考虑了从人群中学习的在线设置，其中标签不断涌入。实证结果表明，我们的方法具有竞争力，通常比最先进的估算器能获得更好的结果。