Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Classifiers with Fenchel-Young Losses: Generalized Entropies,
  Margins, and Algorithms   This paper studies Fenchel-Young losses, a generic way to construct convex
loss functions from a regularization function. We analyze their properties in
depth, showing that they unify many well-known loss functions and allow to
create useful new ones easily. Fenchel-Young losses constructed from a
generalized entropy, including the Shannon and Tsallis entropies, induce
predictive probability distributions. We formulate conditions for a generalized
entropy to yield losses with a separation margin, and probability distributions
with sparse support. Finally, we derive efficient algorithms, making
Fenchel-Young losses appealing both in theory and practice.

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The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
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The categories of this paper are: econ
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
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The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th