Kernel weighted importance sampling for off-policy evaluation in contextual bandits
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Computer Science > Machine Learning
Title:Kernel weighted importance sampling for off-policy evaluation in contextual bandits
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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.15067 [cs.LG] |
| (or arXiv:2607.15067v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.15067
arXiv-issued DOI via DataCite (pending registration)
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