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stat.ML2024
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…
stat.ML2024
Logistic Variational Bayes Revisited
Michael Komodromos, Marina Evangelou, Sarah Filippi
Variational logistic regression is a popular method for approximate Bayesian inference seeing wide-spread use in many areas of machine learning including: Bayesian optimization, re…
stat.ML2024
Weighted-Sum of Gaussian Process Latent Variable Models
James Odgers, Ruby Sedgwick, Chrysoula Kappatou +2
This work develops a Bayesian non-parametric approach to signal separation where the signals may vary according to latent variables. Our key contribution is to augment Gaussian Pro…