Geometry-Aware Block Selection for Sparse Variational Gaussian Processes
Sparse variational Gaussian processes (SVGPs) are a widely used, principled approach to scalable Gaussian process modelling. Their success stems from their theoretically interpretable structure: they provide a lower bound on the marginal likelihood of the full GP while allowing scal- able stochastic optimization. Recent work has explored modifications of the posterior conditional distribution and has shown that such extensions can improve upon this well-established framework. In particular, introducing a block-diagonal structured scal- ing matrix leads to a tighter lower bound on the GP marginal likelihood. We investigate whether selecting blocks that tighten the variational objective imp...
| Authors | Matteo Hoxha |
|---|---|
| Primary category | stat.ML |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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