  We study the adversarial multi-armed bandit problem where partial
observations are available and where, in addition to the loss incurred for each
action, a \emph{switching cost} is incurred for shifting to a new action. All
previously known results incur a factor proportional to the independence number
of the feedback graph. We give a new algorithm whose regret guarantee depends
only on the domination number of the graph. We further supplement that result
with a lower bound. Finally, we also give a new algorithm with improved policy
regret bounds when partial counterfactual feedback is available.
