74 search results - page 3 / 15 » Regret Bounds for Gaussian Process Bandit Problems |
CORR
2011
Springer
13 years 7 days 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...
COLT
2010
Springer
13 years 3 months ago
2010
Springer
Multiarmed bandit problem is a typical example of a dilemma between exploration and exploitation in reinforcement learning. This problem is expressed as a model of a gambler playi...
CORR
2010
Springer
13 years 4 days ago
2010
Springer
We consider an opportunistic spectrum access (OSA) problem where the time-varying condition of each channel (e.g., as a result of random fading or certain primary users' activ...
COLT
2008
Springer
13 years 7 months ago
2008
Springer
We present a modification of the algorithm of Dani et al. [8] for the online linear optimization problem in the bandit setting, which with high probability has regret at most O ( ...
COLT
2010
Springer
13 years 3 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...