Kernel weighted importance sampling for off-policy evaluation in contextual bandits
Joshua Spear, Matthieu Komorowski, Rebecca Pope, Neil J Sebire, Erica E. M. Moodie
cs.LG
Abstract
This article presents a novel estimator for performing off-policy evaluation using only offline data for contextual bandits. The proposed estimator, Kernel-WIS is demonstrated to be asymptotically consistent and to empirically outperform strong baselines (including vanilla weighted importance sampling), particularly under complex conditions including behaviour policy miss-specification. The benefit of Kernel-WIS is derived from combining the bounded property of vanilla weighted importance sampling with the linearity of vanilla importance sampling.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.