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1. Making Paper Reviewing Robust to Bid Manipulation Attacks

   Ruihan Wu, Chuan Guo ( January 2021 , Proceedings of Machine Learning Research)

   Most computer science conferences rely on paper bidding to assign reviewers to papers. Although paper bidding enables high-quality assignments in days of unprecedented submission numbers, it also opens the door for dishonest reviewers to adversarially influence paper reviewing assignments. Anecdotal evidence suggests that some reviewers bid on papers by “friends” or colluding authors, even though these papers are outside their area of expertise, and recommend them for acceptance without considering the merit of the work. In this paper, we study the efficacy of such bid manipulation attacks and find that, indeed, they can jeopardize the integrity of the review process. We develop a novel approach for paper bidding and assignment that is much more robust against such attacks. We show empirically that our approach provides robustness even when dishonest reviewers collude, have full knowledge of the assignment system’s internal workings, and have access to the system’s inputs. In addition to being more robust, the quality of our paper review assignments is comparable to that of current, non-robust assignment approaches. 

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2. PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review

   Stelmakh, Ivan ; Shah, Nihar B ; Singh, Aarti ( March 2019 , Algorithmic Learning Theory)

   We consider the problem of automated assignment of papers to reviewers in conference peer review, with a focus on fairness and statistical accuracy. Our fairness objective is to maximize the review quality of the most disadvantaged paper, in contrast to the popular objective of maximizing the total quality over all papers. We design an assignment algorithm based on an incremental max-flow procedure that we prove is near-optimally fair. Our statistical accuracy objective is to ensure correct recovery of the papers that should be accepted. With a sharp minimax analysis we also prove that our algorithm leads to assignments with strong statistical guarantees both in an objective-score model as well as a novel subjective-score model that we propose in this paper. 

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3. A SUPER* Algorithm to Optimize Paper Bidding in Peer Review

   Fiez, Tanner ; Shah, Nihar ; Ratliff, Lillian ( August 2020 , Uncertainty in artificial intelligence)

   null (Ed.)

   A number of applications involve sequential arrival of users, and require showing each user an ordering of items. A prime example 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 the list 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 develop 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% or more, and is also robust to various real world complexities. 

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4. Mitigating Skewed Bidding for Conference Paper Assignment

   Rozencweig, Inbal ; Meir, Reshef ; Mattei, Nicholas ; Amir, Ofra ( June 2023 , Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems)

   The explosion of conference paper submissions in AI and related fields has underscored the need to improve many aspects of the peer review process, especially the matching of papers and reviewers. Recent work argues that the key to improve this matching is to modify aspects of the bidding phase itself, to ensure that the set of bids over papers is balanced, and in particular to avoid orphan papers, i.e., those papers that receive no bids. In an attempt to understand and mitigate this problem, we have developed a flexible bidding platform to test adaptations to the bidding process. Using this platform, we performed a field experiment during the bidding phase of a medium-size international workshop that compared two bidding methods. We further examined via controlled experiments on Amazon Mechanical Turk various factors that affect bidding, in particular the order in which papers are presented [11, 17]; and information on paper demand [33]. Our results suggest that several simple adaptations, that can be added to any existing platform, may significantly reduce the skew in bids, thereby improving the allocation for both reviewers and conference organizers. 

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5. Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection

   https://doi.org/10.1145/3579335  

   Goldberg, Alexander ; Fanti, Giulia ; Shah, Nihar B. ( February 2023 , Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   There are a number of forums where people participate under pseudonyms. One example is peer review, where the identity of reviewers for any paper is confidential. When participating in these forums, people frequently engage in "batching": executing multiple related tasks (e.g., commenting on multiple papers) at nearly the same time. Our empirical analysis shows that batching is common in two applications we consider -- peer review and Wikipedia edits. In this paper, we identify and address the risk of deanonymization arising from linking batched tasks. To protect against linkage attacks, we take the approach of adding delay to the posting time of batched tasks. We first show that under some natural assumptions, no delay mechanism can provide a meaningful differential privacy guarantee. We therefore propose a "one-sided" formulation of differential privacy for protecting against linkage attacks. We design a mechanism that adds zero-inflated uniform delay to events and show it can preserve privacy. We prove that this noise distribution is in fact optimal in minimizing expected delay among mechanisms adding independent noise to each event, thereby establishing the Pareto frontier of the trade-off between the expected delay for batched and unbatched events. Finally, we conduct a series of experiments on Wikipedia and Bitcoin data that corroborate the practical utility of our algorithm in obfuscating batching without introducing onerous delay to a system. 

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