Multidisciplinary survey of the finite-horizon two-armed bandit with binary responses that unifies models, evaluates designs computationally for moderate vs small horizons, and debunks myths about Bayes-optimal solvability.
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Bayesian optimization uses Gaussian process regression to build a surrogate model and acquisition functions to guide sampling for optimizing costly objective functions, including a new formal generalization of expected improvement to noisy evaluations.
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The Finite-Horizon Two-Armed Bandit Problem with Binary Responses: A Multidisciplinary Survey of the History, State of the Art, and Myths
Multidisciplinary survey of the finite-horizon two-armed bandit with binary responses that unifies models, evaluates designs computationally for moderate vs small horizons, and debunks myths about Bayes-optimal solvability.
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A Tutorial on Bayesian Optimization
Bayesian optimization uses Gaussian process regression to build a surrogate model and acquisition functions to guide sampling for optimizing costly objective functions, including a new formal generalization of expected improvement to noisy evaluations.