Learning from Experts and Multi-armed Bandits are two of the most common settings studied in online learning. Whereas the first setting assumes that the performance of all k actions are revealed at the end of each round, the bandit setting assumes that only the performance of the chosen action is revealed, with corresponding √k-degradation in the provable regret guarantee. In this paper, we study a natural setting which interpolates between the experts and the bandits setting, where choosing an action also reveals some side-information on the performance of some of the other actions. We develop practical algorithms with provable regret guarantees, as well as partially-matching lower bounds. The regret depends on non- trivial graph theoretic properties of the information feedback structure, and has an interesting trade-off between regret optimality and computational efficiency. We end by discussing some of the many open questions that remain.