We study online learnability of a wide class of problems, extending the results of [26] to general notions of performance measure well beyond external regret. Our framework simult...
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
We prove logarithmic regret bounds that depend on the loss L∗ T of the competitor rather than on the number T of time steps. In the general online convex optimization setting, o...
This paper extends the game-theoretic notion of internal regret to the case of on-line potfolio selection problems. New sequential investment strategies are designed to minimize th...
Abstract. We study online regret minimization algorithms in a bicriteria setting, examining not only the standard notion of regret to the best expert, but also the regret to the av...
Eyal Even-Dar, Michael J. Kearns, Yishay Mansour, ...