55 citations · 97 across the 7 of their papers we have counts for
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Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt +1
The kernel function and its hyperparameters are the central model selection choice in a Gaussian proces (Rasmussen and Williams, 2006). Typically, the hyperparameters of the kernel…
The Promises and Pitfalls of Deep Kernel Learning
Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk
Deep kernel learning (DKL) and related techniques aim to combine the representational power of neural networks with the reliable uncertainty estimates of Gaussian processes. One cr…
Convergence of Sparse Variational Inference in Gaussian Processes Regression
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Gaussian processes are distributions over functions that are versatile and mathematically convenient priors in Bayesian modelling. However, their use is often impeded for data with…
Variational Orthogonal Features
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Sparse stochastic variational inference allows Gaussian process models to be applied to large datasets. The per iteration computational cost of inference with this method is $\math…
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…