83 search results - page 5 / 17 » Online Learning: Beyond Regret |
COLT
2008
Springer
13 years 9 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
2006
Springer
13 years 11 months ago
2006
Springer
In an online convex optimization problem a decision-maker makes a sequence of decisions, i.e., chooses a sequence of points in Euclidean space, from a fixed feasible set. After ea...
COLT
2008
Springer
13 years 9 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
2008
Springer
13 years 9 months ago
2008
Springer
This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction guarantees an average pairwise misranking regret of at most t...
COLT
2010
Springer
13 years 5 months ago
2010
Springer
In standard online learning, the goal of the learner is to maintain an average loss that is "not too big" compared to the loss of the best-performing function in a fixed...