I Will Have Order! Optimizing Orders for Fair Reviewer Assignment

Payan, Justin; Zick, Yair

doi:10.48550/arxiv.2108.02126

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…Authors in [5] provide a pseudopolynomial-time algorithm for finding MUW within EF1 for any fixed number of agents for goods, which is exponential in the number of agents. In the paper, [41], the authors present an approximately optimal round-robin order that gives highly efficient (USW) EF1 allocations in the Reviewer Assignment setting; however, the setting is quite different from ours, as we are not concerned with the multiplicity of items.…”

Section: Related Workmentioning

confidence: 98%

EEF1-NN: Efficient and EF1 allocations through Neural Networks

Mishra¹,

Manisha²,

Gujar³

2021

Preprint

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Neural networks have shown state-of-the-art performance in designing auctions, where the network learns the optimal allocations and payment rule to ensure desirable properties. Motivated by the same, we focus on learning fair division of resources, with no payments involved. Our goal is to allocate the items, goods and/or chores efficiently among the fair allocations. By fair, we mean an allocation that is Envy-free (EF). However, such an allocation may not always exist for indivisible resources. Therefore, we consider the relaxed notion, Envy-freeness up to one item (EF1) that is guaranteed to exist. However, it is not enough to guarantee EF1 since the allocation of empty bundles is also EF1. Hence, we add the further constraint of efficiency, maximum utilitarian social welfare (USW). In general finding, USW allocations among EF1 is an NP-Hard problem even when valuations are additive. In this work, we design a network for this task which we refer to as EEF1-NN. We propose an UNet inspired architecture, Lagrangian loss function, and training procedure to obtain desired results. We show that EEF1-NN finds allocation close to optimal USW allocation and ensures EF1 with a high probability for different distributions over input valuations. Compared to existing approaches EEF1-NN empirically guarantees higher USW. Moreover, EEF1-NN is scalable and determines the allocations much faster than solving it as a constrained optimization problem.

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Section: Related Workmentioning

confidence: 98%

EEF1-NN: Efficient and EF1 allocations through Neural Networks

Mishra¹,

Manisha²,

Gujar³

2021

Preprint

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“…However, it can be faster than FairIR and PeerReview4All. Payan and Zick (2021) formulated the RAP as a kind of fair allocation problem for the fair distribution of the reviewer's quality to the proposals. In this study, envy is the main criterion emphasized for fairness, which means if one proposal prefers reviewers assigned to another proposal more than its own, it will envy the other.…”

Section: ∑ 𝑥 𝑖𝑗 𝑛 𝑖=1mentioning

confidence: 99%

Reviewer Assignment Problem: A Systematic Review of the Literature

Meltem¹,

Yanık

Amasyalı

2023

jair

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Appropriate reviewer assignment significantly impacts the quality of proposal evaluation, as accurate and fair reviews are contingent on their assignment to relevant reviewers. The crucial task of assigning reviewers to submitted proposals is the starting point of the review process and is also known as the reviewer assignment problem (RAP). Due to the obvious restrictions of manual assignment, journal editors, conference organizers, and grant managers demand automatic reviewer assignment approaches. Many studies have proposed assignment solutions in response to the demand for automated procedures since 1992. The primary objective of this survey paper is to provide scholars and practitioners with a comprehensive overview of available research on the RAP. To achieve this goal, this article presents an in-depth systematic review of 103 publications in the field of reviewer assignment published in the past three decades and available in the Web of Science, Scopus, ScienceDirect, Google Scholar, and Semantic Scholar databases. This review paper classified and discussed the RAP approaches into two broad categories and numerous subcategories based on their underlying techniques. Furthermore, potential future research directions for each category are presented. This survey shows that the research on the RAP is becoming more significant and that more effort is required to develop new approaches and a framework.

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I Will Have Order! Optimizing Orders for Fair Reviewer Assignment

Cited by 2 publications

References 19 publications

EEF1-NN: Efficient and EF1 allocations through Neural Networks

EEF1-NN: Efficient and EF1 allocations through Neural Networks

Reviewer Assignment Problem: A Systematic Review of the Literature

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