Scalable Bayesian Optimization with Sparse Gaussian Process Models

10/26/2020

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by   Ang Yang, et al.

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This thesis focuses on Bayesian optimization with the improvements coming from two aspects:(i) the use of derivative information to accelerate the optimization convergence; and (ii) the consideration of scalable GPs for handling massive data.