Name
Abstract | ||
|---|---|---|
It is common to see a handful of reviewers reject a highly novel paper, because they view, say, extensive experiments as far more important than novelty, whereas the community as a whole would have embraced the paper. More generally, the disparate mapping of criteria scores to final recommendations by different reviewers is a major source of inconsistency in peer review. In this paper we present a framework --- based on $L(p,q)$-norm empirical risk minimization --- for learning the communityu0027s aggregate mapping. We draw on computational social choice to identify desirable values of $p$ and $q$; specifically, we characterize $p=q=1$ as the only choice that satisfies three natural axiomatic properties. Finally, we implement and apply our approach to reviews from IJCAI 2017. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Artificial Intelligence | Axiom,Computer science,Empirical risk minimization,Computational social choice,Artificial intelligence,Novelty,Machine learning |
DocType | Volume | Citations |
Journal | abs/1808.09057 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ritesh Noothigattu | 1 | 8 | 3.97 |
| Nihar B. Shah | 2 | 1202 | 77.17 |
| Ariel D. Procaccia | 3 | 1875 | 148.20 |