Scalable Bayesian Optimization with Sparse Gaussian Process Models

This thesis seeks to advance the state-of-the-art Bayesian optimization with the improvements coming from two aspects: (1) use of derivative information to accelerate the optimization convergence; and (2) tackle down issues in Bayesian optimization when a large number of function observations and derivative observations are present.