Optimistic Bandit Convex Optimization

Scott Yang, Mehryar Mohri

Neural Information Processing Systems 

We introduce the general and powerful scheme of predicting information re-use in optimization algorithms. This allows us to devise a computationally efficient algorithm for bandit convex optimization with new state-of-the-art guarantees for both Lipschitz loss functions and loss functions with Lipschitz gradients.