142 citations · 600 across the 47 of their papers we have counts for
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Adaptive Multiple Importance Sampling for Gaussian Processes
Xiaoyu Xiong, Václav Šmídl, Maurizio Filippone
In applications of Gaussian processes where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussi…
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…
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)
Maurizio Filippone, Raphael Engler
In applications of Gaussian processes where quantification of uncertainty is of primary interest, it is necessary to accurately characterize the posterior distribution over covaria…