Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits
We present a new algorithm for the contextual bandit learning problem, where the learner repeatedly takes one of $K$ actions in response to the observed context, and observes the reward only for that chosen action. Our method assumes access to an oracle for solving fully supervised cost-sensitive classification problems and achieves the statistically optimal regret guarantee with only $\tilde{O}(\sqrt{KT/\log N})$ oracle calls across all $T$ rounds, where $N$ is the number of policies in the policy class we compete against... (read more)
PDFCode
Tasks
Datasets
RCV1
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods used in the Paper
| METHOD | TYPE | |
|---|---|---|
| 🤖 No Methods Found | Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet |