Bayesian optimisation and Intrinsic Gaussian process on manifold

报告人：      Mu Niu (University of Glasgow)

One of the application of Gaussian process is Bayesian optimisation. It is a sequential design strategy for global optimization of unknown functions or a black box function whose gradients are hard to compute. A cheap proxy function can be built from the Gaussian process prediction. We can make the proxy function exploit uncertainty to balance exploration against exploitation. Bayesian optimization has been widely used to optimize functions defined in Euclidean spaces. In our work, we propose an approach of Bayesian optimization on some interesting domains such as sphere, and Grassmannian.