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.
ProofPaper ↗
Key points
- 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 improves test RMSE and NLL at a fixed inducing-point budget.
- Since the scaling matrix contributes to the variational lower bound through an upper bound on the log-determinant of the full residual covariance matrix, our work makes the following contributions.
- First, we introduce a simple, scalable, geometry-aware algorithm, with explicit computational complexity and low computational overhead, that recovers a large fraction of this log- determinant contribution by selecting an appropriate partition of the data.
Sources (1)
- [1]Geometry-Aware Block Selection for Sparse Variational Gaussian ProcessesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 02:12 PM
Sparse variational Gaussian processes (SVGPs) are a widely used, principled approach to scalable Gaussian process modelling.
In particular, introducing a block-diagonal structured scal- ing matrix leads to a tighter lower bound on the GP marginal likelihood.
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