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Found 6 papers, 3 papers with code
Neural Diffusion Processes
Neural network approaches for meta-learning distributions over functions have desirable properties such as increased flexibility and a reduced complexity of inference.
Validating Gaussian Process Models with Simulation-Based Calibration
Gaussian process priors are a popular choice for Bayesian analysis of regression problems.
Kernel Identification Through Transformers
Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models, as the chosen kernel determines both the inductive biases and prior support of functions under the GP prior.
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain
In the univariate setting, using the kernel spectral representation is an appealing approach for generating stationary covariance functions.
Marginalised Spectral Mixture Kernels with Nested Sampling
Gaussian Process (GPs) models are a rich distribution over functions with inductive biases controlled by a kernel function.
Marginalised Gaussian Processes with Nested Sampling
Learning occurs through the optimisation of kernel hyperparameters using the marginal likelihood as the objective.
