  We present a new anytime algorithm that achieves near-optimal regret for any
instance of finite stochastic partial monitoring. In particular, the new
algorithm achieves the minimax regret, within logarithmic factors, for both
"easy" and "hard" problems. For easy problems, it additionally achieves
logarithmic individual regret. Most importantly, the algorithm is adaptive in
the sense that if the opponent strategy is in an "easy region" of the strategy
space then the regret grows as if the problem was easy. As an implication, we
show that under some reasonable additional assumptions, the algorithm enjoys an
O(\sqrt{T}) regret in Dynamic Pricing, proven to be hard by Bartok et al.
(2011).
