  We derive a new analysis of Follow The Regularized Leader (FTRL) for online
learning with delayed bandit feedback. By separating the cost of delayed
feedback from that of bandit feedback, our analysis allows us to obtain new
results in three important settings. On the one hand, we derive the first
optimal (up to logarithmic factors) regret bounds for combinatorial
semi-bandits with delay and adversarial Markov decision processes with delay
(and known transition functions). On the other hand, we use our analysis to
derive an efficient algorithm for linear bandits with delay achieving
near-optimal regret bounds. Our novel regret decomposition shows that FTRL
remains stable across multiple rounds under mild assumptions on the Hessian of
the regularizer.
