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Eliciting labels from crowds is a potential way to obtain large labeled data.Despite a variety of methods developed for learning from crowds, a keychallenge remains unsolved: \emph{learning from crowds without knowing theinformation structure among the crowds a priori, when some people of the crowdsmake highly correlated mistakes and some of them label effortlessly (e.g.randomly)}. We propose an information theoretic approach, Max-MIG, for jointlearning from crowds, with a common assumption: the crowdsourced labels and thedata are independent conditioning on the ground truth. Max-MIG simultaneouslyaggregates the crowdsourced labels and learns an accurate data classifier.Furthermore, we devise an accurate data-crowds forecaster that employs both thedata and the crowdsourced labels to forecast the ground truth. To the best ofour knowledge, this is the first algorithm that solves the aforementionedchallenge of learning from crowds. In addition to the theoretical validation,we also empirically show that our algorithm achieves the new state-of-the-artresults in most settings, including the real-world data, and is the firstalgorithm that is robust to various information structures. Codes are availableat\hyperlink{https://github.com/Newbeeer/Max-MIG}{https://github.com/Newbeeer/Max-MIG}

Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds