Minimal-envy Conference Paper Assignment: Formulation and a Fast Iterative Algorithm

Tan, Mao; Dai, Zhuocen; Ren, Yuling; Walsh, Toby; Aleksandrov, Martin

doi:10.1109/acait53529.2021.9731163

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Thus, a number of works has explored the idea of assignment fairness, aiming at producing more balanced assignments (Kobren et al, 2019;Stelmakh et al, 2021). Finally, other works explore the ideas of envy-freeness (Tan et al, 2021;Payan, 2022), resistance to lone-wolf strategic behavior (Xu et al, 2019;Dhull et al, 2022), and encouraging various types of diversity (Li et al, 2015;Leyton-Brown et al, 2022).…”

Section: Evaluation Of Similarity-computation Algorithmsmentioning

confidence: 99%

A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences

Stelmakh

Shah

Singh

et al. 2021

AAAI

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Conference peer review constitutes a human-computation process whose importance cannot be overstated: not only it identifies the best submissions for acceptance, but, ultimately, it impacts the future of the whole research area by promoting some ideas and restraining others. A surge in the number of submissions received by leading AI conferences has challenged the sustainability of the review process by increasing the burden on the pool of qualified reviewers which is growing at a much slower rate. In this work, we consider the problem of reviewer recruiting with a focus on the scarcity of qualified reviewers in large conferences. Specifically, we design a procedure for (i) recruiting reviewers from the population not typically covered by major conferences and (ii) guiding them through the reviewing pipeline. In conjunction with the ICML 2020 --- a large, top-tier machine learning conference --- we recruit a small set of reviewers through our procedure and compare their performance with the general population of ICML reviewers. Our experiment reveals that a combination of the recruiting and guiding mechanisms allows for a principled enhancement of the reviewer pool and results in reviews of superior quality compared to the conventional pool of reviews as evaluated by senior members of the program committee (meta-reviewers).

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