2017
Stochastic and Adversarial Online Learning without Hyperparameters
NeurIPS 2017poster
Most online optimization algorithms focus on one of two things: performing well in adversarial settings by adapting to unknown data parameters (such as Lipschitz constants), typically achieving $O(\sqrt{T})$ regret, or performing well in stochastic settings where they can leverage some structure in…