Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Loss Functions, Axioms, and Peer Review   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 inspired by empirical risk minimization (ERM) for
learning the community's aggregate mapping. The key challenge that arises is
the specification of a loss function for ERM. We consider the class of $L(p,q)$
loss functions, which is a matrix-extension of the standard class of $L_p$
losses on vectors; here the choice of the loss function amounts to choosing the
hyperparameters $p, q \in [1,\infty]$. To deal with the absence of ground truth
in our problem, we instead draw on computational social choice to identify
desirable values of the hyperparameters $p$ and $q$. Specifically, we
characterize $p=q=1$ as the only choice of these hyperparameters that satisfies
three natural axiomatic properties. Finally, we implement and apply our
approach to reviews from IJCAI 2017.

22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an