Kernels and Gaussian Processes

All your loss are belong to Bayes

Quantile Propagation for Wasserstein-Approximate Gaussian Processes

Variational Inference for Sparse Gaussian Process Modulated Hawkes Process

Efficient non-parametric Bayesian Hawkes processes

New Tricks for Estimating Gradients of Expectations

Fast Bayesian Intensity Estimation for the Permanental Process

Diffeomorphic Dimensionality Reduction

Sparse Multiscale Gaussian Process Regression

Learning with Transformation Invariant Kernels

Implicit Surface Modelling with a Globally Regularised Basis of Compact Support