Dual Parameterization of Sparse Variational Gaussian Processes¶
This repository is the official implementation of the methods in the publication:
Adam, P.E. Chang, M.E. Khan and A. Solin (2021). Dual Parameterization of Sparse Variational Gaussian Processes. To appear at Advances in Neural Information Processing Systems (NeurIPS). (arxiv)
The paper’s main result shows that an alternative (dual) parameterization for SVGP models leads to a better objective for learning and allows for faster inference via natural gradient descent.
Installation¶
We recommend using Python version 3.7.3 and pip version 20.1.1.
To install the package, run:
$ pip install -e .
Contributing¶
For all correspondence, please contact vincenta@gatsby.ucl.ac.uk.