Efficient factoring of GP likelihoods

1 Inducing variables

See GP inducing variables.

2 Inducing features

See GP inducing features.

3 Spectral and rank sparsity

Loosely speaking, where the functions can be represented in a small number of (hopefully tractable) basis functions. See, for example (Adam et al. 2020; Zammit-Mangion and Cressie 2021).

4 Lattice inputs

See GP with lattice inputs.

5 SVI for Gaussian processes

As seen in Hensman, Fusi, and Lawrence (2013);Salimbeni and Deisenroth (2017).

6 Low rank methods

Represent the GP in terms of a controlled budget of basis functions. See low-rank Gaussian processes.

7 Vecchia factorisation

Approximate the precision matrix by one with a sparse Cholesky factorisation. See Vecchia factorisation.

8 Local

Approximate covariance by nearby predictors.

9 Latent Gaussian Process models

The Edwin V. Bonilla, Krauth, and Dezfouli (2019) set up for Latent Gaussian Process models (“LGPMs”) goes as follows:

We are learning a mapping $\mathit{f}:{\mathbb{R}}^D\to {\mathbb{R}}^P$ from data. The dataset looks like $\mathcal{D}={\{{\mathbf{x}}_n,{\mathbf{y}}_n\}}_{n=1}^N\equiv \{\mathbf{x},\mathbf{y}\}.$ ${\mathbf{x}}_n\in {\mathbb{R}}^D$ is an input vector and ${\mathbf{y}}_n\in {\mathbb{R}}^P$ is an output. We decree that the mapping from inputs to outputs may be expressed by $Q$ underlying latent functions ${\left\{f_j\right\}}_{j=1}^Q.$ We assume that the $Q$ latent functions $\left\{f_j\right\}$ are drawn from (a priori) independent zero-mean Gaussian processes.

$$\begin{array}{rl}p(f_j\mid {\mathit{\theta }}_j)& \sim \mathcal{G}\mathcal{P}(0,{\kappa }_j(\cdot ,\cdot ;{\mathit{\theta }}_j)),j=1,\dots Q,\text{ and }\\ p(\mathbf{f}\mid \mathit{\theta })& =\prod _{j=1}^{Q}p({\mathbf{f}}_{\cdot j}\mid {\mathit{\theta }}_j)\\ & =\prod _{j=1}^Q\mathcal{N}({\mathbf{f}}_{\cdot j};\mathbf{0},{\mathbf{K}}_{\mathbf{x}\mathbf{x}}^j).\end{array}$$ Here $\mathbf{f}$ is the set of all latent function values; ${\mathbf{f}}_{\cdot j}={\left\{f_j\left({\mathbf{x}}_n\right)\right\}}_{n=1}^N$ denotes the values of latent function $j$. The Gram matrix is ${\mathbf{K}}_{\mathrm{xx}}^j$, induced by a covariance kernel, ${\kappa }_j(\cdot ,\cdot ;{\mathit{\theta }}_j)$. The parameters of all kernel functions we call $\mathit{\theta }=\left\{{\mathit{\theta }}_j\right\}.$ Our observation model can have various likelihoods; We call the corresponding parameter $\mathit{\varphi }$. We assume that our multi-dimensional observations $\left\{{\mathbf{y}}_n\right\}$ are i.i.d. given the latent functions $\left\{{\mathbf{f}}_n\right\},$ so that $$p(\mathbf{y}\mid \mathbf{f},\mathit{\varphi })=\prod _{n=1}^{N}p({\mathbf{y}}_n\mid {\mathbf{f}}_{n\cdot },\mathit{\varphi })$$ ${\mathbf{f}}_{n\cdot }=\{f_j({\mathit{x}}_n){\}}_{j=1}^q$ is the set of latent $\mathit{f}$ values upon which ${\mathbf{y}}_n$ depends.

There are several factorizations to note here

1. The prior is factored into latent functions per-coordinate
2. the conditional likelihood is factored over observations (i.e. noise is independent)

If we further factorise the variational approximation in some way this will work out nicely, e.g. into Gaussian mixtures. This works out well for us when we try to devise a system of inference later to minimise the ELBO. TBC.

For now, though, let us examine exactly tractable inference

• Implementing a scalable multi-output GP model with exact inference
• Scaling multi-output Gaussian process models with exact inference

10 References