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Bayesian/minimax duality for adversarial bandits

Posted onMarch 17, 2019March 7, 20201 Comment

The Bayesian approach to learning starts by choosing a prior probability distribution over the unknown parameters of the world. Then, as the learner makes observation, the prior is updated using Bayes rule to form the posterior, which represents the new Continue Reading

CategoriesAdversarial bandits, Bandits, Bayesian bandits, Game theory

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