We consider a model of unreliable or crowdsourced data where there is an underlying set of $n$ binary variables, each evaluator contributes a (possibly unreliable or adversarial) estimate of the values of some subset of $r$ of the variables, and the learner is given the true value of a constant number of variables. We show that, provided an $\alpha$-fraction of the evaluators are "good" (either correct, or with independent noise rate $p < 1/2$), then the true values of a $(1-\epsilon)$ fraction of the $n$ underlying variables can be deduced as long as $\alpha > 1/(2-2p)^r$... (read more)

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