Local Nonstationarity for Efficient Bayesian Optimization

5 Jun 2015Ruben Martinez-Cantin

Bayesian optimization has shown to be a fundamental global optimization algorithm in many applications: ranging from automatic machine learning, robotics, reinforcement learning, experimental design, simulations, etc. The most popular and effective Bayesian optimization relies on a surrogate model in the form of a Gaussian process due to its flexibility to represent a prior over function... (read more)

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