  Partial monitoring is a generic framework for sequential decision-making with
incomplete feedback. It encompasses a wide class of problems such as dueling
bandits, learning with expect advice, dynamic pricing, dark pools, and label
efficient prediction. We study the utility-based dueling bandit problem as an
instance of partial monitoring problem and prove that it fits the time-regret
partial monitoring hierarchy as an easy - i.e. Theta (sqrt{T})- instance. We
survey some partial monitoring algorithms and see how they could be used to
solve dueling bandits efficiently. Keywords: Online learning, Dueling Bandits,
Partial Monitoring, Partial Feedback, Multiarmed Bandits
