65 citations · 220 across the 24 of their papers we have counts for
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cs.LG2018
Scalable GAM using sparse variational Gaussian processes
Vincent Adam, Nicolas Durrande, ST John
Generalized additive models (GAMs) are a widely used class of models of interest to statisticians as they provide a flexible way to design interpretable models of data beyond linea…
cs.LG2018★ 37 cited
Learning Invariances using the Marginal Likelihood
Mark van der Wilk, Matthias Bauer, ST John +1
Generalising well in supervised learning tasks relies on correctly extrapolating the training data to a large region of the input space. One way to achieve this is to constrain the…
stat.ML2018★ 3 cited
Large-Scale Cox Process Inference using Variational Fourier Features
S. T. John, James Hensman
Gaussian process modulated Poisson processes provide a flexible framework for modelling spatiotemporal point patterns. So far this had been restricted to one dimension, binning to…