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PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review
We consider the problem of automated assignment of papers to reviewers i...
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What Constitutes Peer Review of Data: A survey of published peer review guidelines
Since a number of journals specifically focus on the review and publicat...
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Day of the week submission effect for accepted papers in Physica A, PLOS ONE, Nature and Cell
The particular day of the week when an event occurs seems to have unexpe...
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On Strategyproof Conference Peer Review
We consider peer review in a conference setting where there is typically...
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Deep Paper Gestalt
Recent years have witnessed a significant increase in the number of pape...
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Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
We consider three important challenges in conference peer review: (i) re...
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Most memory efficient distributed super points detection on core networks
The super point, a host which communicates with lots of others, is a kin...
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A SUPER* Algorithm to Optimize Paper Bidding in Peer Review
A number of applications involve sequential arrival of users, and require showing each user an ordering of items. A prime example (which forms the focus of this paper) is the bidding process in conference peer review where reviewers enter the system sequentially, each reviewer needs to be shown the list of submitted papers, and the reviewer then "bids" to review some papers. The order of the papers shown has a significant impact on the bids due to primacy effects. In deciding on the ordering of papers to show, there are two competing goals: (i) obtaining sufficiently many bids for each paper, and (ii) satisfying reviewers by showing them relevant items. In this paper, we begin by developing a framework to study this problem in a principled manner. We present an algorithm called SUPER*, inspired by the A* algorithm, for this goal. Theoretically, we show a local optimality guarantee of our algorithm and prove that popular baselines are considerably suboptimal. Moreover, under a community model for the similarities, we prove that SUPER* is near-optimal whereas the popular baselines are considerably suboptimal. In experiments on real data from ICLR 2018 and synthetic data, we find that SUPER* considerably outperforms baselines deployed in existing systems, consistently reducing the number of papers with fewer than requisite bids by 50-75 various real world complexities.
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