1 research outputs found
Bandits with Partially Observable Offline Data
We study linear contextual bandits with access to a large, partially
observable, offline dataset that was sampled from some fixed policy. We show
that this problem is closely related to a variant of the bandit problem with
side information. We construct a linear bandit algorithm that takes advantage
of the projected information, and prove regret bounds. Our results demonstrate
the ability to take full advantage of partially observable offline data.
Particularly, we prove regret bounds that improve current bounds by a factor
related to the visible dimensionality of the contexts in the data. Our results
indicate that partially observable offline data can significantly improve
online learning algorithms. Finally, we demonstrate various characteristics of
our approach through synthetic simulations