  We investigate the problem of active learning on a given tree whose nodes are
assigned binary labels in an adversarial way. Inspired by recent results by
Guillory and Bilmes, we characterize (up to constant factors) the optimal
placement of queries so to minimize the mistakes made on the non-queried nodes.
Our query selection algorithm is extremely efficient, and the optimal number of
mistakes on the non-queried nodes is achieved by a simple and efficient mincut
classifier. Through a simple modification of the query selection algorithm we
also show optimality (up to constant factors) with respect to the trade-off
between number of queries and number of mistakes on non-queried nodes. By using
spanning trees, our algorithms can be efficiently applied to general graphs,
although the problem of finding optimal and efficient active learning
algorithms for general graphs remains open. Towards this end, we provide a
lower bound on the number of mistakes made on arbitrary graphs by any active
learning algorithm using a number of queries which is up to a constant fraction
of the graph size.
