263 search results - page 2 / 53 » Regret Bounds for Prediction Problems |
JMLR
2012
11 years 7 months ago
2012
Contextual bandit learning is a reinforcement learning problem where the learner repeatedly receives a set of features (context), takes an action and receives a reward based on th...
COCOON
2006
Springer
13 years 8 months ago
2006
Springer
Abstract. While the complexity of min-max and min-max regret versions of most classical combinatorial optimization problems has been thoroughly investigated, there are very few stu...
ALT
2007
Springer
14 years 1 months ago
2007
Springer
Algorithms based on upper-confidence bounds for balancing exploration and exploitation are gaining popularity since they are easy to implement, efficient and effective. In this p...
JMLR
2010
12 years 11 months ago
2010
Bandit algorithms are concerned with trading exploration with exploitation where a number of options are available but we can only learn their quality by experimenting with them. ...
ECCC
2010
13 years 4 months ago
2010
Suppose a decision maker has to purchase a commodity over time with varying prices and demands. In particular, the price per unit might depend on the amount purchased and this pri...