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stat.ML2026
Symmetry Guarantees Statistic Recovery in Variational Inference
Daniel Marks, Dario Paccagnan, Mark van der Wilk
Variational inference (VI) is a central tool in modern machine learning, used to approximate an intractable target density by optimising over a tractable family of distributions. A…
stat.ML2026
Inverse-Free Sparse Variational Gaussian Processes
Stefano Cortinovis, Laurence Aitchison, Stefanos Eleftheriadis +1
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 decompositi…