BSI fits an environment simulator from bandit data and propagates parameter uncertainty to produce asymptotically valid confidence intervals for mean reward under arbitrary evaluation policies, including black-box adaptive ones.
Stable thompson sampling: Valid inference via variance inflation.arXiv preprint arXiv:2505.23260, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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Develops IPW-Z estimation framework for misspecified contextual bandits, establishing consistency and asymptotic normality under scaled inverse-propensity convergence for marginal moment targets.
citing papers explorer
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Bandit Simulation for Average Reward Inference
BSI fits an environment simulator from bandit data and propagates parameter uncertainty to produce asymptotically valid confidence intervals for mean reward under arbitrary evaluation policies, including black-box adaptive ones.
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Statistical Inference for Misspecified Contextual Bandits
Develops IPW-Z estimation framework for misspecified contextual bandits, establishing consistency and asymptotic normality under scaled inverse-propensity convergence for marginal moment targets.