22 search results - page 1 / 5 » High-Probability Regret Bounds for Bandit Online Linear Opti... |
COLT
2008
Springer
13 years 6 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 ( ...
NIPS
2007
13 years 6 months ago
2007
In the online linear optimization problem, a learner must choose, in each round, a decision from a set D ⊂ Rn in order to minimize an (unknown and changing) linear cost function...
JMLR
2010
12 years 11 months ago
2010
This work deals with four classical prediction settings, namely full information, bandit, label efficient and bandit label efficient as well as four different notions of regret: p...
CORR
2011
Springer
12 years 12 months ago
2011
Springer
The analysis of online least squares estimation is at the heart of many stochastic sequential decision-making problems. We employ tools from the self-normalized processes to provi...
COLT
2008
Springer
13 years 6 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...