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Rates of Convergence for Sparse Variational Gaussian Process Regression

Excellent variational approximations to Gaussian process posteriors have been developed which avoid the $mathcalOłeft(N^3i̊ght)$ scaling with dataset size $N$. They reduce the computational cost to $mathcalOłeft(NM^2g̊ht)$, with $Młl N$ the number of …

Bayesian Layers: A Module for Neural Network Uncertainty

Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes

Learning Invariances using the Marginal Likelihood

Concrete problems for autonomous vehicle safety: advantages of Bayesian deep learning

Convolutional Gaussian Processes

Understanding Probabilistic Sparse Gaussian Process Approximations

Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models