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Stochastic Process Bandits: Upper Confidence Bounds Algorithms via Generic Chaining
The paper considers the problem of global optimization in the setup of stochastic process bandits.
Optimization for Gaussian Processes via Chaining
In this paper, we consider the problem of stochastic optimization under a bandit feedback model.
Gaussian Process Optimization with Mutual Information
In this paper, we analyze a generic algorithm scheme for sequential global optimization using Gaussian processes.
Parallel Gaussian Process Optimization with Upper Confidence Bound and Pure Exploration
We prove theoretical upper bounds on the regret with batches of size K for this procedure which show the improvement of the order of sqrt{K} for fixed iteration cost over purely sequential versions.
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