66 search results - page 1 / 14 » The Nonstochastic Multiarmed Bandit Problem |
COLT
2008
Springer
13 years 9 months ago
2008
Springer
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...
SIAMCOMP
2002
13 years 7 months ago
2002
Abstract. In the multiarmed bandit problem, a gambler must decide which arm of K nonidentical slot machines to play in a sequence of trials so as to maximize his reward. This class...
JMLR
2012
11 years 10 months ago
2012
We develop a new tool for data-dependent analysis of the exploration-exploitation trade-off in learning under limited feedback. Our tool is based on two main ingredients. The fi...
CORR
2012
Springer
12 years 3 months ago
2012
Springer
We present a bandit algorithm, SAO (Stochastic and Adversarial Optimal), whose regret is, essentially, optimal both for adversarial rewards and for stochastic rewards. Specifical...
ECML
2005
Springer
14 years 1 months ago
2005
Springer
The multi-armed bandit problem for a gambler is to decide which arm of a K-slot machine to pull to maximize his total reward in a series of trials. Many real-world learning and opt...