HIGHLIGHTS
- who: Kaufmann Emilie and Garivier Aurélien from the (UNIVERSITY) have published the paper: Learning the distribution with largest mean: two bandit frameworks, in the Journal: (JOURNAL)
- what: The authors compare the behaviors of the sampling rule of each algorithm as well as the complexity terms associated to each problem. It motivates the consideration of a different optimization problem in a bandit model: rather than continuously changing its website, the company may prefer to experiment during a testing phase only, which is aimed at identifying the best version, and then to use that one consistently . . .
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