Bandit algorithms generate data for downstream inference, but adaptive sampling biases post-bandit sample means. We analyze this bias for stable index algorithms, including UCB1 and its generalizations, and derive sharp leading-order expressions for the sample-mean bias and expected $Z$-statistic, in bandit experiments...
Bandit algorithms generate data for downstream inference, but adaptive sampling biases post-bandit sample means. We analyze this bias for stable index algorithms, including UCB1 and its generalizations, and derive sharp leading-order expressions for the sample-mean bias and expected $Z$-statistic. Our characterization...
This work develops a novel expansion representation for the OS value and proves that truncating this expansion yields a simulation-based algorithm that implements an optimal stopping policy with computational complexity scaling polynomially in the time horizon and the underlying dimension.
Yi-Lun Chen, David A. Goldberg· Stochastic Systems· 0 citations
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