Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Linear Regression with Limited Observation   We consider the most common variants of linear regression, including Ridge,
Lasso and Support-vector regression, in a setting where the learner is allowed
to observe only a fixed number of attributes of each example at training time.
We present simple and efficient algorithms for these problems: for Lasso and
Ridge regression they need the same total number of attributes (up to
constants) as do full-information algorithms, for reaching a certain accuracy.
For Support-vector regression, we require exponentially less attributes
compared to the state of the art. By that, we resolve an open problem recently
posed by Cesa-Bianchi et al. (2010). Experiments show the theoretical bounds to
be justified by superior performance compared to the state of the art.

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The categories of this paper are: adap-org
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
3
The categories of this paper are: physics
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The categories of this paper are: eess
6
The categories of this paper are: astro-ph
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ