Elad Hazan

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization.

We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative grad...

Logarithmic regret algorithms for online convex optimization

Competing in the dark: An efficient algorithm for bandit linear optimization

We introduce an efficient algorithm for the problem of online linear optimization in the bandit setting which achieves the optimal O*(√T)regret. The setting is a natural general...

Adaptive Online Gradient Descent

We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori...

Logarithmic Regret Algorithms for Online Convex Optimization

Extracting certainty from uncertainty: regret bounded by variation in costs