4 papers · 1 filter
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…
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…
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
Many machine learning models require setting a parameter that controls their size before training, e.g. number of neurons in DNNs, or inducing points in GPs. Increasing capacity ty…
Continuous Bayesian Model Selection for Multivariate Causal Discovery
Anish Dhir, Ruby Sedgwick, Avinash Kori +2
Current causal discovery approaches require restrictive model assumptions in the absence of interventional data to ensure structure identifiability. These assumptions often do not…