280 citations · 1.1k across the 43 of their papers we have counts for
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Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li +2
A method for large scale Gaussian process classification has been recently proposed based on expectation propagation (EP). Such a method allows Gaussian process classifiers to be t…
Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
Thang D. Bui, José Miguel Hernández-Lobato, Yingzhen Li +2
Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wid…
Black-box -divergence Minimization
José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland +3
Black-box alpha (BB-) is a new approximate inference method based on the minimization of -divergences. BB- scales to large datasets because it can be implemented using sto…
Stochastic Expectation Propagation
Yingzhen Li, Jose Miguel Hernandez-Lobato, Richard E. Turner
Expectation propagation (EP) is a deterministic approximation algorithm that is often used to perform approximate Bayesian parameter learning. EP approximates the full intractable…