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- W2117593143 abstract "We consider a sequential adaptive allocation problem which is formulated as a traditional two armed bandit problem but with one important modification: at each time step t, before selecting which arm to pull, the decision maker has access to a random variable X <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>t</sub> which provides information on the reward in each arm. Performance is measured as the fraction of time an inferior arm (generating lower mean reward) is pulled. We derive a minimax lower bound that proves that in the absence of sufficient statistical diversity in the distribution of the covariate X, a property that we shall refer to as lack of persistent excitation, no policy can improve on the best achievable performance in the traditional bandit problem without side information." @default.
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- W2117593143 date "2011-03-01" @default.
- W2117593143 modified "2023-10-02" @default.
- W2117593143 title "A Note on Performance Limitations in Bandit Problems With Side Information" @default.
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- W2117593143 doi "https://doi.org/10.1109/tit.2011.2104450" @default.
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