  The sample complexity of active learning under the realizability assumption
has been well-studied. The realizability assumption, however, rarely holds in
practice. In this paper, we theoretically characterize the sample complexity of
active learning in the non-realizable case under multi-view setting. We prove
that, with unbounded Tsybakov noise, the sample complexity of multi-view active
learning can be $\widetilde{O}(\log\frac{1}{\epsilon})$, contrasting to
single-view setting where the polynomial improvement is the best possible
achievement. We also prove that in general multi-view setting the sample
complexity of active learning with unbounded Tsybakov noise is
$\widetilde{O}(\frac{1}{\epsilon})$, where the order of $1/\epsilon$ is
independent of the parameter in Tsybakov noise, contrasting to previous
polynomial bounds where the order of $1/\epsilon$ is related to the parameter
in Tsybakov noise.
