Scalable Bayesian Optimization with Sparse Gaussian Process Models
arXiv:2010.13301
Abstract
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.
Thesis
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