  Large-scale labeled dataset is the indispensable fuel that ignites the AI
revolution as we see today. Most such datasets are constructed using
crowdsourcing services such as Amazon Mechanical Turk which provides noisy
labels from non-experts at a fair price. The sheer size of such datasets
mandates that it is only feasible to collect a few labels per data point. We
formulate the problem of test-time label aggregation as a statistical
estimation problem of inferring the expected voting score. By imitating workers
with supervised learners and using them in a doubly robust estimation
framework, we prove that the variance of estimation can be substantially
reduced, even if the learner is a poor approximation. Synthetic and real-world
experiments show that by combining the doubly robust approach with adaptive
worker/item selection rules, we often need much lower label cost to achieve
nearly the same accuracy as in the ideal world where all workers label all data
points.
