  The problem of "approximating the crowd" is that of estimating the crowd's
majority opinion by querying only a subset of it. Algorithms that approximate
the crowd can intelligently stretch a limited budget for a crowdsourcing task.
We present an algorithm, "CrowdSense," that works in an online fashion to
dynamically sample subsets of labelers based on an exploration/exploitation
criterion. The algorithm produces a weighted combination of a subset of the
labelers' votes that approximates the crowd's opinion.
