Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes
The need for matrix decompositions (inverses) is often named as a major impediment to scaling Gaussian process (GP) models, even in efficient approximations. To address this, Van der Wilk et al. (2020) introduced a variational lower bound that can be computed without these costly operations. We improve this bound by 1) simplifying it by removing the need for iterative procedures, and 2) making it more numerically stable. While these improvements do not result in a procedure that is faster in wall-clock time than existing variational bounds, they are likely to be necessary steps along the way.
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