346 citations · 1.1k across the 15 of their papers we have counts for
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stat.ML2015★ 55 cited
MCMC for Variationally Sparse Gaussian Processes
James Hensman, Alexander G. de G. Matthews, Maurizio Filippone +1
Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable research effort has been made into attacking three issues with GP models: how to compu…
stat.ML2015★ 4 cited
On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes
Alexander G. de G. Matthews, James Hensman, Richard E. Turner +1
The variational framework for learning inducing variables (Titsias, 2009a) has had a large impact on the Gaussian process literature. The framework may be interpreted as minimizing…