Scaling Gaussian Processes with Derivative Information Using Variational Inference

no code implementations • NeurIPS 2021 • Misha Padidar, Xinran Zhu, Leo Huang, Jacob R. Gardner, David Bindel

We demonstrate the full scalability of our approach on a variety of tasks, ranging from a high dimensional stellarator fusion regression task to training graph convolutional neural networks on Pubmed using Bayesian optimization.

Bayesian Optimization Gaussian Processes +2