Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Private Algorithms for Learning Large-Margin Halfspaces   We present new differentially private algorithms for learning a large-margin
halfspace. In contrast to previous algorithms, which are based on either
differentially private simulations of the statistical query model or on private
convex optimization, the sample complexity of our algorithms depends only on
the margin of the data, and not on the dimension. We complement our results
with a lower bound, showing that the dependence of our upper bounds on the
margin is optimal.

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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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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: math-ph
2
The categories of this paper are: math
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The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex