Howdy folks,

GPyTorch provides Gaussian likelihood objects for fixed noise (FixedNoiseGaussianLikelihood) and for multi-task models (MultitaskGaussianLikelihood). I was wondering if someone could provide me some guidance on how to get a fixed noise multi-task Gaussian likelihood?

Thanks in advance

Galto

Feature request

Our Scipy optimizer wrapper should explicitly handle unconnected gradients.

Motivation

Currently, the GPflow Scipy optimizer wrapper (gpflow.optimizers.Scipy) simply errors (with one of the typically unhelpful TensorFlow error messages) when one of the passed-in variables is not connected to the loss to be minimized. This can easily happen in a valid case, for exa

There are a variety of interesting optimisations that can be performed on kernels of the form

k(x, z) = w_1 * k_1(x, z) + w_2 * k_2(x, z) + ... + w_L k_L(x, z)

A naive recursive implementation in terms of the current Sum and Scaled kernels hides opportunities for parallelism in the computation of each term, and the summation over terms.

Notable examples of kernels with th