Gaussian Processes

Definition. A Gaussian process is random function $f$ on a set $X$ such that for any $x_1,..,x_n \in X$ , the vector $f(x_1),..,f(x_n)$ is multivariate Gaussian.

May refer to a random function / distribution, depending on the context.

Gaussian processes are characterized by

a mean function $m(x) = \E(f(x))$ ,
a kernel (covariance) function $k(x, x') = \Cov(f(x), f(x'))$ ,

Notation: $f \~ \f{GP}(m, k)$ .

The kernel $k$ must be positive (semi-)definite.

Gaussian Process Regression

Takes

a prior Gaussian Process $\f{GP}(m, k)$
and data $(x_1, y_1), .., (x_n, y_n) \in X \x \R$ ,

giving the posterior (conditional) Gaussian process $\f{GP}(\hat{m}, \hat{k})$ .

The functions $\hat{m}$ and $\hat{k}$ may be explicitly expressed in terms of $m$ and $k$ .

Motivating Example

Consider a Simple Dynamical System: a Pendulum

$\htmlData{fragment-index=0,class=fragment}{ x_0 } \qquad \htmlData{fragment-index=1,class=fragment}{ x_1 = x_0 + f(x_0)\Delta t } \qquad \htmlData{fragment-index=2,class=fragment}{ x_2 = x_1 + f(x_1)\Delta t } \qquad \htmlData{fragment-index=3,class=fragment}{ .. }$

Consider a Simple Dynamical System: a Pendulum

$x_0 \qquad x_1 = x_0 + f(x_0)\Delta t \qquad x_2 = x_1 + f(x_1)\Delta t \qquad ..$

Consider a Simple Dynamical System: a Pendulum

f: \R \x \R \to \R \x \R

assume $f$ unknown, model $f$ as a GP.

$x_0 \qquad x_1 = x_0 + f(x_0)\Delta t \qquad x_2 = x_1 + f(x_1)\Delta t \qquad ..$

Pendulum Dynamics: Phase Portrait

Need priors on non-Euclidean domains!

And they have to be geometry-aware.

Be able to: evaluate $k(x, x')$ , differentiate it, sample $\mathrm{GP}(0, k)$ .

Non-Euclidean Domains

Manifolds
e.g. in physics (robotics)

Graphs
e.g. for road networks.

Spaces of graphs
e.g. for drug design

Standard Priors

Matérn Gaussian Processes

$\htmlData{class=fragment fade-out,fragment-index=6}{ \footnotesize \mathclap{ k_{\nu, \kappa, \sigma^2}(x,x') = \sigma^2 \frac{2^{1-\nu}}{\Gamma(\nu)} \del{\sqrt{2\nu} \frac{\abs{x-x'}}{\kappa}}^\nu K_\nu \del{\sqrt{2\nu} \frac{\abs{x-x'}}{\kappa}} } } \htmlData{class=fragment d-print-none,fragment-index=6}{ \footnotesize \mathclap{ k_{\infty, \kappa, \sigma^2}(x,x') = \sigma^2 \exp\del{-\frac{\abs{x-x'}^2}{2\kappa^2}} } }$ $\sigma^2$ : variance $\kappa$ : length scale $\nu$ : smoothness
$\nu\to\infty$ : RBF kernel (Gaussian, Heat, Diffusion)

$\nu = 1/2$

$\nu = 3/2$

$\nu = 5/2$

$\nu = \infty$

How to generalize these
to non-Euclidean settings?

Distance-based Approach

$k_{\infty, \kappa, \sigma^2}(x,x') = \sigma^2\exp\del{-\frac{|x - x'|^2}{2\kappa^2}}$

$k_{\infty, \kappa, \sigma^2}^{(d)}(x,x') = \sigma^2\exp\del{-\frac{d(x,x')^2}{2\kappa^2}}$

It is geometry-aware, but...

For manifolds. Not well-defined unless the manifold is isometric to a Euclidean space.
(Feragen et al. 2015)

For graphs. Not well-defined unless nodes can be isometrically embedded into a Hilbert space.
(Schonberg, 1930s)

For spaces of graphs. What is a space of graphs?!

Need a different generalization

Another Approach: Stochastic Partial Differential Equations

$\htmlData{class=fragment,fragment-index=0}{ \underset{\t{Matérn}}{\undergroup{\del{\frac{2\nu}{\kappa^2} - \Delta}^{\frac{\nu}{2}+\frac{d}{4}} f = \c{W}}} }$ $\Delta$ : Laplacian $\c{W}$ : Gaussian white noise $d$ : dimension

Generalizes well

Geometry-aware: respects to symmetries of the space

Implicit: no formula for the kernel

Making It Explicit:
Compact Manifolds

Examples: $\bb{S}_d$ , $\bb{T}^d$

Solution for Compact Riemannian Manifolds

The solution is a Gaussian process with kernel $\htmlData{fragment-index=2,class=fragment}{ k_{\nu, \kappa, \sigma^2}(x,x') = \frac{\sigma^2}{C_{\nu, \kappa}} \sum_{n=0}^\infty \Phi_{\nu, \kappa}(\lambda_n) f_n(x) f_n(x') }$

$\lambda_n, f_n$ : eigenvalues and eigenfunctions of the Laplace–Beltrami operator

\htmlData{fragment-index=4,class=fragment}{ \Phi_{\nu, \kappa}(\lambda) = \begin{cases} \htmlData{fragment-index=5,class=fragment}{ \del{\frac{2\nu}{\kappa^2} - \lambda}^{-\nu-\frac{d}{2}} } & \htmlData{fragment-index=5,class=fragment}{ \nu < \infty \text{ --- Matérn} } \\ \htmlData{fragment-index=6,class=fragment}{ e^{-\frac{\kappa^2}{2} \lambda} } & \htmlData{fragment-index=6,class=fragment}{ \nu = \infty \text{ --- Heat (RBF)} } \end{cases} }

How to get $\lambda_n, f_n$ ?

Answer I: Discretize the Problem

Mesh the manifold, consider the discretized Laplace–Beltrami (a matrix).

Answer II: Use Algebraic Structure

For manifolds with rich group of symmetries, termed homogeneous spaces.

These include

Lie groups, like rotations $\mathrm{SO}(n)$ and unitary matrices $\mathrm{SU}(n)$ ,
«Proper» homogeneous spaces, e.g. spheres $\mathbb{S}_d$ , projective spaces $\mathrm{RP}_d$ , Stiefel manifolds $\mathrm{V}(k, n)$ or Grassmannians $\mathrm{Gr}(n, r)$ .

$\lambda_n, f_n$ are connected to representation theory of the group of symmetries.

Examples: The Values of $k_{1/2}(\htmlStyle{color:rgb(255, 19, 0)!important}{\bullet},\.)$ on Compact Riemannian Manifolds

Circle
(Lie group)

Sphere
(homogeneous space)

Dragon
(general manifold)

Examples: Samples from $\mathrm{GP}(0, k_{\infty})$ on Compact Homgeneous Spaces

Sphere

\mathbb{S}_2

Torus

\mathbb{T}^2 = \bb{S}_1 \x \bb{S}_1

Projective plane

\mathrm{RP}^2

Driven by generalized random phase Fourier features (GRPFF).

Making It Explicit:
Non-compact Manifolds

Examples: $\bb{H}_d$ , $\mathrm{SPD}(d)$

Random Fourier Features (RFF)

Take stationary $k$ . Assume for simplicity $k(x, x) = 1$ . Then

\htmlData{data-id=rffformula,class=fragment}{ \begin{aligned} k(x, x') &= \int_{\R^d} S(\lambda) e^{2 \pi i \innerprod{x - x'}{\lambda}} \d \lambda \\ & \htmlData{class=fragment}{ \approx \frac{1}{L} \sum_{l=1}^L e^{2 \pi i \innerprod{x - x'}{\lambda_l}} \qquad \lambda_l \sim S(\lambda) } \end{aligned} }

$k$ is RBF $\implies$ $S(\lambda)$ is Gaussian. $k$ is Matérn $\implies$ $S(\lambda)$ is $t$ distributed.

Random Fourier Features (RFF) for Symmetric Spaces

\htmlData{data-id=rffformula}{ \begin{aligned} k(x, x') &= \htmlData{fragment-index=1,class=fragment fade-out disappearing-fragment}{ \int_{\R^d} } \htmlData{fragment-index=1,class=fragment fade-in appearing-fragment}{ \int_{\R^{\color{blue}r}} } S(\lambda) \htmlData{fragment-index=2,class=fragment fade-out disappearing-fragment}{ e^{2 \pi i \innerprod{x - x'}{\lambda}} } \htmlData{fragment-index=2,class=fragment fade-in appearing-fragment}{ {\color{blue}\pi^{(\lambda)}(x, x')} } \htmlData{fragment-index=3,class=fragment fade-in appearing-fragment}{ {\color{blue}c(\lambda)^{-2}} } \d \lambda \\ &\approx \frac{1}{L} \sum_{l=1}^L \htmlData{fragment-index=4,class=fragment fade-out disappearing-fragment}{ e^{2 \pi i \innerprod{x - x'}{\lambda_l}} } \htmlData{fragment-index=4,class=fragment fade-in appearing-fragment}{ {\color{blue}\pi^{(\lambda_l)}(x, x')} } \qquad \lambda_l \sim \htmlData{fragment-index=5,class=fragment fade-in appearing-fragment}{ {\color{blue}c(\lambda)^{-2}} } S(\lambda) \end{aligned} }

$k$ is RBF $\implies$ $S(\lambda)$ is Gaussian. $k$ is Matérn $\implies$ $S(\lambda)$ is $t$ distributed.

• $\pi^{(\lambda)}$ is an explicit integral. • $c(\lambda)$ has closed form.
• $c(\lambda)^{-2} S(\lambda)$ is a non-standard, potentially unnormalized, density.

Examples: The Values of $k_{\infty}(\htmlStyle{color:rgb(0, 0, 0)!important}{\bullet},\.)$ on Non-compact Manifolds

Hyperbolic space

\bb{H}_2

Space of positive definite matrices $\f{SPD}(2)$

Random Fourier Features for Sampling

\htmlData{class=fragment}{ k(x, x') \approx \frac{1}{L} \sum_{l=1}^L e^{2 \pi i \innerprod{x - x'}{\lambda_l}} \qquad \lambda_l \sim S(\lambda) }

Since $e^{2 \pi i \innerprod{x - x'}{\lambda_l}} = e^{2 \pi i \innerprod{x}{\lambda_l}} \overline{e^{2 \pi i \innerprod{x'}{\lambda_l}}}$ , the above is an inner product.

$\htmlData{class=fragment}{ f(x) \approx \frac{1}{\sqrt{L}} \sum_{l=1}^L w_l e^{2 \pi i \innerprod{x}{\lambda_l}} \qquad w_l \sim \mathrm{N}(0, 1) \qquad \lambda_l \sim S(\lambda) }$

Random Fourier Features for Sampling: symmetric spaces

k(x, x') \approx \frac{1}{L} \sum_{l=1}^L \htmlData{fragment-index=1,class=fragment fade-out disappearing-fragment}{ e^{2 \pi i \innerprod{x - x'}{\lambda_l}} } \htmlData{fragment-index=1,class=fragment fade-in appearing-fragment}{ {\color{blue}\pi^{(\lambda_l)}(x, x')} } \qquad \htmlData{fragment-index=2,class=fragment fade-in appearing-fragment}{ {\color{blue}c(\lambda)^{-2}} } \lambda_l \sim S(\lambda)

Since $\htmlData{fragment-index=3,class=fragment fade-out disappearing-fragment}{ e^{2 \pi i \innerprod{x - x'}{\lambda_l}} } \htmlData{fragment-index=3,class=fragment fade-in appearing-fragment}{ {\color{blue}\pi^{(\lambda_l)}(x, x')} } \!\,= \htmlData{fragment-index=4,class=fragment fade-out disappearing-fragment}{ e^{2 \pi i \innerprod{x}{\lambda_l}} \overline{e^{2 \pi i \innerprod{x'}{\lambda_l}}} } \htmlData{fragment-index=4,class=fragment fade-in appearing-fragment}{ {\color{blue} \pi^{(\lambda_l)}(x, ?) \overline{\pi^{(\lambda_l)}(x', ?)} } }$ , the above is an inner product. $\vphantom{ e^{2 \pi i \innerprod{x - x'}{\lambda_l}} {\color{blue}\pi^{(\lambda_l)}(x, x')} \!\,= e^{2 \pi i \innerprod{x}{\lambda_l}} \overline{e^{2 \pi i \innerprod{x'}{\lambda_l}}} {\color{blue} \pi^{(\lambda_l)}(x, ?) \overline{\pi^{(\lambda_l)}(x', ?)} } } \sout{ {\color{blue}\pi^{(\lambda_l)}(x, x')} = {\color{blue} \pi^{(\lambda_l)}(x, ?) \overline{\pi^{(\lambda_l)}(x', ?)} } }$ , $\vphantom{ e^{2 \pi i \innerprod{x - x'}{\lambda_l}} {\color{blue}\pi^{(\lambda_l)}(x, x')} \!\,= e^{2 \pi i \innerprod{x}{\lambda_l}} \overline{e^{2 \pi i \innerprod{x'}{\lambda_l}}} {\color{blue} \pi^{(\lambda_l)}(x, ?) \overline{\pi^{(\lambda_l)}(x', ?)} } } {\color{blue}\pi^{(\lambda_l)}(x, x')} = \E_{h \sim \mu_H} e^{\innerprod{i \lambda + \rho}{\,a(h, x)}} \overline{ e^{\innerprod{i \lambda + \rho}{\,a(h, x')}} },$ where

$f(x) \approx \frac{1}{\sqrt{L}} \sum_{l=1}^L w_l e^{2 \pi i \innerprod{x}{\lambda_l}} \qquad w_l \sim \mathrm{N}(0, 1) \qquad \lambda_l \sim S(\lambda)$

• the vector $\rho$ and the function $a(\cdot, \cdot)$ are known, • $\mu_H$ is samplabale.
• Monte Carlo approximation of the expectation $\E_{h \sim \mu_H}$ is an inner product.

Examples: Samples from $\mathrm{GP}(0, k_{\infty})$ on Non-compact Homgeneous Spaces

Hyperbolic space

\bb{H}_2

Space of positive definite matrices $\f{SPD}(2)$

Applications: Bayesian Optimization in Robotics

Joint postures: $\mathbb{T}^d$

Rotations: $\mathbb{S}_3$ , $\operatorname{SO}(3)$

Manipulability: $\operatorname{SPD(3)}$

Making It Explicit:
Node Sets of Graphs

Matérn Kernels on Weighted Undirected Graphs

SPDE turns into a stochastic linear system. The solution has kernel $\htmlData{fragment-index=2,class=fragment}{ k_{\nu, \kappa, \sigma^2}(i, j) = \frac{\sigma^2}{C_{\nu, \kappa}} \sum_{n=0}^{\abs{V}-1} \Phi_{\nu, \kappa}(\lambda_n) \mathbf{f_n}(i)\mathbf{f_n}(j) }$

$\lambda_n, \mathbf{f_n}$ : eigenvalues and eigenvectors of the Laplacian matrix

\htmlData{fragment-index=4,class=fragment}{ \Phi_{\nu, \kappa}(\lambda) = \begin{cases} \htmlData{fragment-index=5,class=fragment}{ \del{\frac{2\nu}{\kappa^2} - \lambda}^{-\nu} } & \htmlData{fragment-index=5,class=fragment}{ \nu < \infty \text{ --- Matérn} } \\ \htmlData{fragment-index=6,class=fragment}{ e^{-\frac{\kappa^2}{2} \lambda} } & \htmlData{fragment-index=6,class=fragment}{ \nu = \infty \text{ --- Heat (RBF)} } \end{cases} }

Traffic Speed Extrapolation on a Road Network

(a) Prediction

(b) Standard Deviation

Making It Explicit:
Spaces of Graphs

Spaces of Graphs

Consider the set of all unweighted graphs with $n$ nodes. It is finite!
How to give it a geometric structure? Make it into a space?

Graphs define geometry of discrete sets. We make it into a node set of a graph!

Space of graphs with $n = 3$ nodes
as the $3$ -dimensional cube

Spaces of Graphs

Beyond functions of actual graphs $f\big(\smash{\includegraphics[height=2.5em,width=1.0em]{figures/gg2.svg}}\big)$ , it is useful to consider functions of equivalence classes of graphs: $f\big(\big\{\smash{\includegraphics[height=2.5em,width=1.0em]{figures/gg2.svg}}, \smash{\includegraphics[height=2.5em,width=1.0em]{figures/gg3.svg}}, \smash{\includegraphics[height=2.5em,width=1.0em]{figures/gg4.svg}}\big\}\big)$ .

Space of equivalence classes for $n=4$
as the quotient graph of the $4$ -d cube

Application: Drug Design

Conclusion

Implementation

https://geometric-kernels.github.io/

Thank you!

Slides:

viacheslav.borovitskiy@gmail.com https://vab.im

A Homogeneous Space Example: Sphere $\mathbb{S}_n$

$\begin{aligned} \htmlData{fragment-index=1,class=fragment}{k(x,y)} &\htmlData{fragment-index=2,class=fragment}{ = \frac{\sigma^2}{C_{\nu, \kappa}} \sum_{n=0}^\infty \Phi_{\nu, \kappa}(\lambda_n) f_n(x) \mathrlap{f_n(y)} \htmlData{fragment-index=3,class=fragment}{ \obr{\phantom{f_n(y)}}{\hspace{-0.5cm}\text{spherical harmonics}\hspace{-0.5cm}} } } \\ &\htmlData{fragment-index=4,class=fragment}{= \frac{\sigma^2}{C_{\nu, \kappa}} \sum_{n=0}^\infty \Phi_{\nu, \kappa}(\lambda_n) \mathrlap{\del{\sum_{k=1}^{d_n} f_{n, k}(x) f_{n, k}(y)}} \htmlData{fragment-index=5,class=fragment}{ \ubr{\phantom{\del{\sum_{k=1}^{d_n} f_{n, k}(x) f_{n, k}(y)}}}{\hspace{-0.7cm} C_{n, d} \cdot \phi_{n, d}(x, y) \text{ --- zonal spherical harmonics}\hspace{-0.7cm}} } } \end{aligned}$

The last equation

is much more efficient for computing $k$ ,
generalizes: zonal spherical harmonics → zonal ??????? ???????spherical functions,
unfortunately, is unfit for sampling. Or is it?

Examples: The Values of $k_{1/2}(\htmlStyle{color:rgb(255, 19, 0)!important}{\bullet},\.)$ on Compact Riemannian Manifolds

Circle
(Lie group)

Sphere
(homogeneous space)

Dragon
(general manifold)

Sampling: Generalized Random Phase Fourier Features

Zonal spherical harmonics satisfy reproducing property:

\begin{aligned} \htmlData{class=fragment}{ \phi_{n, d}(x, y) } & \htmlData{class=fragment}{ = C_{n, d} \int_{\mathbb{S^n}} \phi_{n, d}(x, u) \phi_{n, d}(y, u) d u } \\ & \htmlData{class=fragment}{ \approx \frac{C_{n, d}}{L} \sum_{l=1}^L \phi_{n, d}(x, u_l) \phi_{n, d}(y, u_l), } && \htmlData{class=fragment}{ u_l \stackrel{\text{i.i.d.}}{\sim} \mathrm{U}(\mathbb{S}_n). } \end{aligned}

Hence $\sqrt{C_{n, d}/L} \cdot \phi_{n, d}(x, \v{u})$ forms an approximate feature transform.

This enables efficient sampling without knowing $f_n$ . And this generalizes!

Examples: Samples from $\mathrm{GP}(0, k_{\infty})$ on Compact Homgeneous Spaces

Sphere

\mathbb{S}_2

Torus

\mathbb{T}^2 = \bb{S}_1 \x \bb{S}_1

Projective plane

\mathrm{RP}^2

Talk at the University of Oxford

Geometric

Gaussian Processes

Viacheslav Borovitskiy (Slava)