Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Supervised Learning: No Loss No Cry   Supervised learning requires the specification of a loss function to
minimise. While the theory of admissible losses from both a computational and
statistical perspective is well-developed, these offer a panoply of different
choices. In practice, this choice is typically made in an \emph{ad hoc} manner.
In hopes of making this procedure more principled, the problem of
\emph{learning the loss function} for a downstream task (e.g., classification)
has garnered recent interest. However, works in this area have been generally
empirical in nature.
  In this paper, we revisit the {\sc SLIsotron} algorithm of Kakade et al.
(2011) through a novel lens, derive a generalisation based on Bregman
divergences, and show how it provides a principled procedure for learning the
loss. In detail, we cast {\sc SLIsotron} as learning a loss from a family of
composite square losses. By interpreting this through the lens of \emph{proper
losses}, we derive a generalisation of {\sc SLIsotron} based on Bregman
divergences. The resulting {\sc BregmanTron} algorithm jointly learns the loss
along with the classifier. It comes equipped with a simple guarantee of
convergence for the loss it learns, and its set of possible outputs comes with
a guarantee of agnostic approximability of Bayes rule. Experiments indicate
that the {\sc BregmanTron} substantially outperforms the {\sc SLIsotron}, and
that the loss it learns can be minimized by other algorithms for different
tasks, thereby opening the interesting problem of \textit{loss transfer}
between domains.

12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph