Web[{"kind":"Article","id":"GK3A111AM.1","pageId":"GC6A10R74.1","layoutDeskCont":"BL_NEWS","teaserText":"Deep dive","bodyText":"Deep dive Why rural India is struggling ... WebMulti-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges Sofia S. Villar, Jack Bowden and James Wason Abstract. Multi-armed bandit problems (MABPs) are a special type of op timal control problem well suited to model resource allocation under uncer tainty in a wide variety of contexts.
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Gaussian Process Thompson sampling for Bayesian optimization …
http://www.columbia.edu/~mo2499/files/ts-mnl-poster.pdf WebThe worst-case regret of TS-MNL + optimistic sampling with M= d1+ClogKesamples is: Regret(T) = Oe d3=2 p T Matches regret bound for linear TS bandits [1] Additional p dfactor vs Bayesian regret: deviation of random sampling addressed in worst-case regret analysis In case of a finite number of items (actions), i.e., N how do you spell continual