Motivated by crowdsourced computation, peer-grading, and recommendation systems, Braverman, Mao and Weinberg [STOC16] studied the emph{query} and emph{round} complexity of fundamental problems such as finding the maximum (textsc{max}), finding all elements above a certain value (textsc{threshold-$v$}) or computing the top$-k$ elements (textsc{Top}-$k$) in a noisy environment. For example, consider the task of selecting papers for a conference. This task is challenging due the crowdsourcing nature of peer reviews: the results of reviews are noisy and it is necessary to parallelize the review process as much as possible. We study the noisy value model and the noisy comparison model: In the emph{noisy value model}, a reviewer is asked to evaluate a single element: What is the value of paper $i$? (eg accept). In the emph{noisy comparison model} (introduced in the seminal work of Feige, Peleg, Raghavan and Upfal [SICOMP94]) a reviewer is asked to do a pairwise comparison: Is paper $i$ better than paper $j$? In this paper, we show optimal worst-case query complexity for the textsc{max},textsc{threshold-$v$} and textsc{Top}-$k$ problems. For textsc{max} and textsc{Top}-$k$, we obtain optimal worst-case upper and lower bounds on the round vs query complexity in both models. For textsc{threshold}-$v$, we obtain optimal query complexity and nearly-optimal round complexity, where $k$ is the size of the output) for both models. We then go beyond the worst-case and address the question of the importance of knowledge of the instance by providing, for a large range of parameters, instance-optimal algorithms with respect to the query complexity. Furthermore, we show that the value model is strictly easier than the comparison model.
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