most citedState Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes

4 citations · 4 across the 2 of their papers we have counts for

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cs.LG2022

Fantasizing with Dual GPs in Bayesian Optimization and Active Learning

Paul E. Chang, Prakhar Verma, ST John +3

Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning. Their drawbacks are poor scaling with dat…

cs.LG2020

Fast Variational Learning in State-Space Gaussian Process Models

Paul E. Chang, William J. Wilkinson, Mohammad Emtiyaz Khan +1

Gaussian process (GP) regression with 1D inputs can often be performed in linear time via a stochastic differential equation formulation. However, for non-Gaussian likelihoods, thi…