Scalable Bayesian Optimization with Sparse Gaussian Process Models

This work addresses the challenge of efficient optimization in large-scale machine learning applications, but it appears incremental as it builds on existing Bayesian optimization methods.

The thesis tackled the problem of accelerating Bayesian optimization convergence and handling massive data by using derivative information and scalable Gaussian process models, resulting in improved efficiency and scalability.