74 search results - page 4 / 15 » Regret Bounds for Gaussian Process Bandit Problems |
ML
2002
ACM
13 years 5 months ago
2002
ACM
Reinforcement learning policies face the exploration versus exploitation dilemma, i.e. the search for a balance between exploring the environment to find profitable actions while t...
COLT
2007
Springer
13 years 12 months ago
2007
Springer
Abstract. Considering one-dimensional continuum-armed bandit problems, we propose an improvement of an algorithm of Kleinberg and a new set of conditions which give rise to improve...
NIPS
2007
13 years 7 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...
CORR
2008
Springer
13 years 5 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...
LION
2010
Springer
13 years 9 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...