5 search results - page 1 / 1 » Regret Bounds for Sleeping Experts and Bandits |
COLT
2008
Springer
13 years 6 months ago
2008
Springer
We study on-line decision problems where the set of actions that are available to the decision algorithm vary over time. With a few notable exceptions, such problems remained larg...
COLT
2005
Springer
13 years 6 months ago
2005
Springer
External regret compares the performance of an online algorithm, selecting among N actions, to the performance of the best of those actions in hindsight. Internal regret compares ...
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...
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...
LION
2010
Springer
13 years 8 months ago
2010
Springer
Abstract. Algorithm selection is typically based on models of algorithm performance learned during a separate offline training sequence, which can be prohibitively expensive. In r...