hidden-convex-losses

This paper proves that online gradient descent achieves optimal √T regret for hidden-convex losses under a Hessian compatibility condition, resolving open questions in adversarial online learning. It also extends results to one-point bandit feedback with a T^{3/4} expected regret bound.