22 search results - page 2 / 5 » High-Probability Regret Bounds for Bandit Online Linear Opti... |
COLT
2010
Springer
13 years 5 months ago
2010
Springer
Bandit convex optimization is a special case of online convex optimization with partial information. In this setting, a player attempts to minimize a sequence of adversarially gen...
CORR
2004
Springer
13 years 7 months ago
2004
Springer
We study a general online convex optimization problem. We have a convex set S and an unknown sequence of cost functions c1, c2, . . . , and in each period, we choose a feasible po...
CORR
2011
Springer
13 years 2 months ago
2011
Springer
In this paper we study the online learning problem involving rested and restless multiarmed bandits with multiple plays. The system consists of a single player/user and a set of K...
ALT
2008
Springer
14 years 4 months ago
2008
Springer
Abstract. We consider an upper confidence bound algorithm for Markov decision processes (MDPs) with deterministic transitions. For this algorithm we derive upper bounds on the onl...
NIPS
2008
13 years 9 months ago
2008
This paper examines the generalization properties of online convex programming algorithms when the loss function is Lipschitz and strongly convex. Our main result is a sharp bound...