Inverse-Free Sparse Variational Gaussian Processes
Gaussian processes (GPs) offer appealing properties but are costly to train at scale. Sparse variational GP (SVGP) approximations reduce cost yet still rely on Cholesky decompositions of kernel matrices, ill-suited to low-precision, massively parallel hardware. While one can construct valid variational bounds that rely only on matrix multiplications (matmuls) via an auxiliary matrix parameter, optimising them with off-the-shelf first-order methods is challenging. We make the inverse-free approach practical by proposing a better-conditioned bound and deriving a matmul-only natural-gradient update for the auxiliary parameter, markedly improving stability and convergence. We further provide ...
| Authors | Stefano Cortinovis, Laurence Aitchison, Stefanos Eleftheriadis et al. |
|---|---|
| Primary category | stat.ML |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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