  We consider the problem of online boosting for regression tasks, when only
limited information is available to the learner. We give an efficient regret
minimization method that has two implications: an online boosting algorithm
with noisy multi-point bandit feedback, and a new projection-free online convex
optimization algorithm with stochastic gradient, that improves state-of-the-art
guarantees in terms of efficiency.
