Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Consistent Multilabel Ranking through Univariate Losses   We consider the problem of rank loss minimization in the setting of
multilabel classification, which is usually tackled by means of convex
surrogate losses defined on pairs of labels. Very recently, this approach was
put into question by a negative result showing that commonly used pairwise
surrogate losses, such as exponential and logistic losses, are inconsistent. In
this paper, we show a positive result which is arguably surprising in light of
the previous one: the simpler univariate variants of exponential and logistic
surrogates (i.e., defined on single labels) are consistent for rank loss
minimization. Instead of directly proving convergence, we give a much stronger
result by deriving regret bounds and convergence rates. The proposed losses
suggest efficient and scalable algorithms, which are tested experimentally.

37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph