Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Beyond Disagreement-based Agnostic Active Learning   We study agnostic active learning, where the goal is to learn a classifier in
a pre-specified hypothesis class interactively with as few label queries as
possible, while making no assumptions on the true function generating the
labels. The main algorithms for this problem are {\em{disagreement-based active
learning}}, which has a high label requirement, and {\em{margin-based active
learning}}, which only applies to fairly restricted settings. A major challenge
is to find an algorithm which achieves better label complexity, is consistent
in an agnostic setting, and applies to general classification problems.
  In this paper, we provide such an algorithm. Our solution is based on two
novel contributions -- a reduction from consistent active learning to
confidence-rated prediction with guaranteed error, and a novel confidence-rated
predictor.

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The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg