Efficient Optimal Learning for Contextual Bandits

Details Efficient Optimal Learning for Contextual Bandits Efficient Optimal Learning for Contextual Bandits

2011-06-13

arxiv.org/abs/1106.2369v1

Computer Science

Learning

Scientific paper

We address the problem of learning in an online setting where the learner repeatedly observes features, selects among a set of actions, and receives reward for the action taken. We provide the first efficient algorithm with an optimal regret. Our algorithm uses a cost sensitive classification learner as an oracle and has a running time $\mathrm{polylog}(N)$, where $N$ is the number of classification rules among which the oracle might choose. This is exponentially faster than all previous algorithms that achieve optimal regret in this setting. Our formulation also enables us to create an algorithm with regret that is additive rather than multiplicative in feedback delay as in all previous work.

Affiliated with

Also associated with

No associations

Rating

Efficient Optimal Learning for Contextual Bandits does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Efficient Optimal Learning for Contextual Bandits, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Efficient Optimal Learning for Contextual Bandits will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-429036