In the multi-armed bandit problem, a gambler must decide which arm
of K non-identical slot machines to play in a sequence of trials
so as to maximize his reward.
This classical problem has received much attention because of the
simple model it provides of the trade-off between
exploration ... more >>>

We introduce a new technique which enables a learner without access to
hidden information to learn nearly as well as a learner with access to
hidden information. We apply our technique to solve an open problem
of Maass and Turan, showing that for any concept class F the least ... more >>>

We investigate a variant of the Probably Almost Correct learning model
where the learner has to learn from ambiguous information. The
ambiguity is introduced by assuming that the learner does not receive
single instances with their correct labels as training data, but that
the learner receives ... more >>>