263 search results - page 8 / 53 » Regret Bounds for Prediction Problems |
ICML
2007
IEEE
14 years 10 months ago
2007
IEEE
We provide a framework to exploit dependencies among arms in multi-armed bandit problems, when the dependencies are in the form of a generative model on clusters of arms. We find ...
CORR
2008
Springer
13 years 10 months ago
2008
Springer
We consider bandit problems involving a large (possibly infinite) collection of arms, in which the expected reward of each arm is a linear function of an r-dimensional random vect...
COLT
2010
Springer
13 years 7 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...
NIPS
2008
13 years 11 months ago
2008
This paper examines the generalization properties of online convex programming algorithms when the loss function is Lipschitz and strongly convex. Our main result is a sharp bound...
COLT
2010
Springer
13 years 7 months ago
2010
Springer
We describe online algorithms for learning a rotation from pairs of unit vectors in Rn . We show that the expected regret of our online algorithm compared to the best fixed rotati...