667 citations · 1.5k across the 26 of their papers we have counts for
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Benchmarking the Neural Linear Model for Regression
Sebastian W. Ober, Carl Edward Rasmussen
The neural linear model is a simple adaptive Bayesian linear regression method that has recently been used in a number of problems ranging from Bayesian optimization to reinforceme…
Approximate Inference for Fully Bayesian Gaussian Process Regression
Vidhi Lalchand, Carl Edward Rasmussen
Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function. The classical approach entails maximizing the marginal…
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman +1
We identify a new variational inference scheme for dynamical systems whose transition function is modelled by a Gaussian process. Inference in this setting has either employed comp…
Rates of Convergence for Sparse Variational Gaussian Process Regression
David R. Burt, Carl E. Rasmussen, Mark van der Wilk
Excellent variational approximations to Gaussian process posteriors have been developed which avoid the scaling with dataset size . They reduce the…