We present Trieste, an open-source Python package for Bayesian optimizat...
As Gaussian processes mature, they are increasingly being deployed as pa...
Software packages like TensorFlow and PyTorch are designed to support li...
Recent work in scalable approximate Gaussian process regression has disc...
We introduce GPflux, a Python library for Bayesian deep learning with a
...
We propose a lower bound on the log marginal likelihood of Gaussian proc...
Many machine learning models require a training procedure based on runni...
Thompson Sampling (TS) with Gaussian Process (GP) models is a powerful t...
One obstacle to the use of Gaussian processes (GPs) in large-scale probl...
The use of Gaussian process models is typically limited to datasets with...
Bayesian optimisation is a powerful tool to solve expensive black-box
pr...
Deep learning has been at the foundation of large improvements in image
...