Kernel weighted importance sampling for off-policy evaluation in contextual bandits
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.
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
“Fuzzy title match (0.73): “Kernel weighted importance sampling for off-policy evaluatio” ≈ “microsoft/semantic-kernel””
- LinkedLinked via arxiv author · 85%Joshua Spear →
“Kernel weighted importance sampling for off-policy evaluation in contextual bandits”
- LinkedLinked via arxiv author · 85%Matthieu Komorowski →
“Kernel weighted importance sampling for off-policy evaluation in contextual bandits”
- LinkedLinked via arxiv author · 85%Rebecca Pope →
“Kernel weighted importance sampling for off-policy evaluation in contextual bandits”
- LinkedLinked via arxiv author · 85%Neil J Sebire →
“Kernel weighted importance sampling for off-policy evaluation in contextual bandits”
- LinkedLinked via arxiv author · 85%Erica E. M. Moodie →
“Kernel weighted importance sampling for off-policy evaluation in contextual bandits”