Gaussian processes are useful for probabilistic modeling of unknown functions. We investigate and characterize the behavior of the hyperparameters of Gaussian processes, which will guide us toward useful heuristics with respect to optimization and numerical stability.

1 Introduction

## Introduction

Gaussian processes (GPs) are the canonical method for Bayesian modeling of functions, especially in applications, where data is scarce. A GP combines flexible modeling with uncertainty quantification, which is valuable in many downstream tasks.

GPs are probability distributions over functions and useful non-parametric models for probabilistic regression. A GP is defined as a set of random variables, any finite number of which is jointly Gaussian distributed. They are fully specified by a mean function $m$ and a covariance function (kernel) $k$, which are used to compute the mean vector and the covariance matrix of the joint distribution of a finite number of random variables.This article is not a full introduction to Gaussian processes, but it instead provides a characterization of the typical hyperparameters that control a GP, which can then be used to establish heuristics for optimization and numerical stability that are used in practice. For a lower-level visual introduction to the concept of Gaussian processes, we refer to .

A function can be considered an infinitely long vector $f_1,f_2,\ldots$ of function values at corresponding input locations $\mathbf x_1,~\mathbf x_2, \ldots$. One of the most straightforward ways to place a distribution on these function values would be a Gaussian distribution. However, the Gaussian distribution is only defined for finite-dimensional vectors and not for functions. The Gaussian process generalizes the Gaussian distribution to this setting by describing any finite subset of (random) function values by a joint Gaussian distribution. This is practically relevant, since we are usually only interested in finite training and test sets. If we partition the set of all function values into training set $\mathbf{f}$, test set $\mathbf{f}_*$ and "other" function values, the marginalization property of the Gaussian allows us to integrate out infinitely many "other" function values to obtain a finite joint Gaussian distribution $p(\mathbf f, \mathbf f_*)$ with mean $\mathbf m$ and covariance matrix $\mathbf K$ given by

\mathbf m = \begin{bmatrix} \mathbf m_f\\ \mathbf m_* \end{bmatrix}\,,\quad \mathbf K = \begin{bmatrix} \mathbf K_{ff} & \mathbf K_{f*}\\ \mathbf K_{*f} & \mathbf K_{**} \end{bmatrix}\,,

respectively. The entries of the mean vector and the covariance matrix can be computed using the mean and covariance functions of the GP, such that $m_i = m(\mathbf x_i)$, $K_{ij} = k(\mathbf x_i,\mathbf x_j),$ where $\mathbf x_i, \mathbf x_j$ are from the training or test sets. Gaussian conditioning on this joint distribution yields the Gaussian predictive distribution of function values $\mathbf f_*$ at test time as

\begin{aligned} p(\mathbf f_* | \mathbf f) &= \mathcal N(\mathbf \mu, \mathbf\Sigma)\\ \mathbf \mu &= \mathbf m_* + \mathbf K_{*f}\mathbf K_{ff}^{-1}(\mathbf f - \mathbf m_f)\\ \mathbf\Sigma &= \mathbf K_{**}- \mathbf K_{*f}\mathbf K_{ff}^{-1}\mathbf K_{f*}\,. \end{aligned}

The GP is a useful and flexible model, and good software is available that allows us to use GPs as black-box for classification, regression and unsupervised learning. This black-box treatment of GPs can work very well, but it is not uncommon to encounter problems with local optima during optimization, numerical stability etc. The GP is a model that allows us to have a closer look at the sources of these issues, which are often related to the GP's hyperparameters that control the model. In order to sort out potential issues with the inference in GPs, an intuition of what hyperparameters mean will be helful. In the following, we will shed some light on the meaning and interpretation of various hyperparameters. Furthermore, we use this intuition to address some common problems that arise when using Gaussian processes. In our discussion, we will focus on stationary covariance functions, which are used commonly.

2 Setting

## Regression setting

We consider a regression problem

y = f(\mathbf{x}) + \epsilon\,,

where $\mathbf{x}\in\mathbb{R}^D$, $y\in\mathbb{R}$, and $\epsilon\sim \mathcal N(0,\sigma_n^2)$ is i.i.d. Gaussian measurement noise. We place a GP prior on the unknown function $f$, such that the generative process is

\begin{aligned} & p(f) = GP(m,k)\\ & p(y|f, \mathbf{x}) = \mathcal N(y|f(\mathbf{x}), \sigma_n^2)\,, \end{aligned}

where $p(f)$ is the GP prior and $p(y|f, \mathbf{x})$ is the likelihood. Moreover, $m$ and $k$ are the mean and covariance functions of the GP, respectively.

For training inputs $\mathbf X = [\mathbf x_1, \ldots, \mathbf x_N]$ and corresponding (noisy) training targets $\mathbf y = [y_1,\ldots, y_N]$ we obtain the predictive distribution

\begin{aligned} & p(f_*|\mathbf X, \mathbf y, \mathbf x_*) = \mathcal N(f_*|\mu_*, \sigma_*^2)\\ & \mu_* = m(\mathbf x_*) + k(\mathbf x_*, \mathbf X)( k(\mathbf X, \mathbf X) + \sigma_n^2\mathbf I)^{-1}(\mathbf y - m(\mathbf X))\\ & \sigma_*^2 = k(\mathbf x_*, \mathbf x_*) - k(\mathbf x_*, \mathbf X)( k(\mathbf X, \mathbf X) + \sigma_n^2\mathbf I)^{-1} k(\mathbf X, \mathbf x_*) \end{aligned}

at a test point $\mathbf{x}_*$.

2.1 Mean and covariance functions

### Mean and covariance functions

The prior mean function $m(\cdot)$ describes the average function under the GP distribution before seeing any data. Therefore, it offers a straightforward way to incorporate prior knowledge about the function we wish to model. For example, any kind of (idealized) mechanical or physics model can be used as a prior. The GP would then model the discrepancy between this prior and real-world data. In the absence of this type of prior knowledge, a common choice is to set the prior mean function to zero, i.e., $m(\cdot)\equiv 0$. This is the setting we consider in the following. However, everything we will discuss also holds for general prior mean functions.

The covariance function $k(\mathbf{x}, \mathbf{x}^\prime)$ computes the covariance $cov[f(\mathbf{x}), f(\mathbf{x}^\prime)]$ between the corresponding function values by evaluating the covariance function $k$ at the corresponding inputs $\mathbf{x}, \mathbf{x}^\prime$ (kernel trick ). Practically, the covariance function encodes structural assumptions about the class of functions we wish to model. These assumptions are generally at a high level and may include periodicity or differentiability.For a light, but helpful overview of different covariance functions and their properties see , or for a more comprehensive overview. Typically, the mean and covariance functions are parameterized. These parameters are the hyperparameters of the GP.

2.2 Marginal likelihood and GP training

### Marginal likelihood and GP training

To train the GP, we maximize the marginal likelihood with respect to the GP hyperparameters, i.e., the parameters of the mean and covariance functions, which we summarize by $\mathbf{\theta}$. The marginal likelihood in our setting (regression with Gaussian likelihood) can be computed in closed form and is given by

\begin{aligned} p(\mathbf{y}|\mathbf{X}, \mathbf{\theta}) &= \int p(\mathbf{y}|f, \mathbf{X}) p(f|\mathbf{X}) df\\ &= \int \mathcal N(\mathbf{y}|f(\mathbf{X}), \sigma_n^2\mathbf{I} ) \mathcal N(f(\mathbf{X})| \mathbf{0}, \mathbf{K}) df(\mathbf{X})\\ &= \mathcal N(\mathbf{y}|\mathbf{0}, \mathbf{K} + \sigma^2_n\mathbf{I})\,, \end{aligned}

where $\mathbf K$ is the kernel matrix with $K_{ij}=k(\mathbf x_i, \mathbf x_j)$. $\mathbf X = [\mathbf x_1,\ldots, \mathbf x_N]^T$ are the training inputs, and $\mathbf y = [y_1,\ldots, y_N]^T$ are the corresponding training targets.

The hyperparameters $\mathbf\theta$ appear non-linearly in the kernel matrix $\mathbf{K}$, and a closed-form solution to maximizing the marginal likelihood cannot be found in general. In practice, we use gradient-based optimization algorithms (e.g., conjugate gradients or BFGS) to find a (local) optimum of the marginal likelihood.

Remark. Maximizing the marginal likelihood behaves much better than finding maximum likelihood or maximum a-posteriori point estimates \begin{aligned} &\underset{f(\mathbf{X}),~\sigma_n}{\text{argmax}}\,\, p(\mathbf{y}|f(\mathbf{X}), \sigma^2_n \mathbf{I}) \,, \quad&\text{maximum likelihood}\\ &\underset{f(\mathbf{X}),~\sigma_n}{\text{argmax}}\,\, p(\mathbf{y}|f(\mathbf{X}), \sigma^2_n \mathbf{I})p(f(\mathbf{X})|\mathbf\theta) \,, \quad&\text{maximum a posteriori} \end{aligned} if we were to compute them.Making a connection to a linear regression setting, the parameters of a GP would be the function values $f(\mathbf X)$ themselves. These two approaches would lead to overfitting, since it is possible to get arbitrarily high likelihoods by placing the function values $f(\mathbf X)$ on top of the observations $\mathbf y$ and letting the the noise $\sigma_n$ tend to zero. In contrast, the marginal likelihood does not fit function values directly, but integrates them out, i.e., technically we cannot "overfit" as no fitting happens. By averaging (integrating out) the direct model parameters, i.e., the function values, the marginal likelihood automatically trades off data fit and model complexity . Choose a model that is too inflexible, and the marginal likelihood $p(\mathbf{y}|\mathbf{X}, \mathbf{\theta})$ will be low because few functions in the prior fit the data. A model that is too flexible spreads its density over too many datasets, and so $p(\mathbf{y}|\mathbf{X}, \mathbf{\theta})$ will also be low. By maximizing the marginal likelihood, we find point estimates of the hyperparameters controlling the mean and covariance functionUsing a point estimate for the hyperparameters is a convenience only. The model may still look like overfitting if data is scarce. However, given that usually there are only few hyperparameters, the posterior is usually peaked, and a point estimate is not a terrible approximation. To be robust to overfitting effects caused by point estimates of the hyperparameters, we could perform Bayesian inference over them as well by, for example, Markov Chain Monte Carlo., such that the data is explained well by the GP.

3 Stationary covariance functions and hyperparameters

## Stationary covariance functions and hyperparameters

Hyperparameters play a central role in Gaussian processes, as they control high-level properties of the prior through the mean and covariance functions. The hyperparameters may also cause some practical issues (e.g., numerical instability) during inference. However, the GP often allows for an interpretation of its small number of hyperparameters, which can be used to address those practical issues.

In the following, we will exclusively focus our discussion on stationary covariance functions (kernels) for interpreting these hyperparameters. Stationarity implies that the covariance function only depends on distances $\|\mathbf{x} - \mathbf{x}^\prime\|$ of the corresponding inputs, and not on the location of the individual data points. This means that if the inputs are close to each other, the corresponding function values are strongly correlated. Therefore, stationary covariance functions can also be written as $k(\tau)$, where $\tau = \|\mathbf{x} - \mathbf{x}^\prime\|$.

Commonly used stationary covariance functions are the Gaussian (squared exponential, exponentiated quadratic, RBF) and the Matern family:

\begin{aligned} \begin{array}{ll} \text{Gaussian: } & k(\mathbf{x}, \mathbf{x}^\prime) = k(\tau) = \sigma_f^2 \exp\left(-\frac{\tau^2}{2l^2}\right)\\ \text{Matern 3/2: } & k(\mathbf{x}, \mathbf{x}^\prime) = k(\tau) = \sigma_f^2\left(1 + \frac{\sqrt{3}\tau}{l}\right)\exp\left(-\frac{\sqrt{3}\tau}{l}\right)\\ \text{Matern 5/2: } & k(\mathbf{x}, \mathbf{x}^\prime) = k(\tau) = \sigma_f^2\left(1 + \frac{\sqrt{5}\tau}{l} + \frac{5\tau^2}{3l^2}\right)\exp\left(-\frac{\sqrt{5}\tau}{l}\right)\\ \end{array} \end{aligned}

The parameters of these covariance functions are the lengthscale $l$ and the signal variance $\sigma_f^2$. The parameters of the covariance function are the hyperparameters of the GP and control the GP distribution.Here, we discuss the case of isotropic covariance functions, i.e., covariance functions with a single lengthscale parameter for all input dimensions. It is straightforward to extend the discussion to ARD (automatic relevance determination) settings with individual lengthscales per input dimension. We do not do this here to keep the notation simple.

4 Interpretation of hyperparameters

## Interpretation of the hyperparameters

In the following, we will provide insights into what these hyperparameters mean and how they can be interpreted. These insights can not only help us initializing the optimization of the hyperparameters to 'meaningful' values; they also allow us to identify, explain and address some of the problems we encounter when working with Gaussian processes. Throughout the following, we make the assumption that the zero mean and a stationary covariance function make sense.

4.1 Lengthscales

### Lengthscales

Stationary covariance functions typically contain the term \frac{\tau}{l} = \frac{\|\mathbf{x} - \mathbf{x}^\prime\|}{l}\,, where $l$ is a lengthscale parameter. Using a lengthscale is equivalent to rescaling the inputs $\mathbf x$ by $1/l$ before computing the kernel. If we choose a long lengthscale, the inputs will be mapped to a narrower range of values, which will correlate their corresponding outputs more strongly. As a consequence, there will be less variation in the function values, and the functions we can describe will look close to linear (for the range of input data). Short lengthscales do the opposite and make function values uncorrelated (see Figure 1 for an illustration). Longer lengthscales cause long-range correlations, whereas for short lengthscales, function values are strongly correlated only if their respective inputs are very close to each other. This allows functions to vary strongly and display more flexibility in the range of the data. Short lengthscales make the GP more expressive in the sense that it will be able to fit many more datasets.

4.2 Signal variance

### Signal variance

The signal variance parameter $\sigma_f^2$ allows us to say something about the amplitude of the function we model. The covariance function can generally be written as k(\mathbf{x}, \mathbf{x}^\prime) = \sigma_f^2 \tilde k(\mathbf{x}, \mathbf{x}^\prime)\,, where we defined $\tilde k$ to be the normalized (stationary) covariance function with $\tilde k(\mathbf{x}, \mathbf{x}^\prime)\in[0,1]$. Therefore, for a stationary covariance function, we obtain the (marginal) variance of a function value as \begin{aligned} \text{var}[f(\mathbf{x})] &= k(\mathbf{x}, \mathbf{x}) = \sigma_f^2 \tilde k(\mathbf{x}, \mathbf{x})\\ & = \sigma_f^2 \tilde k(\mathbf{x}- \mathbf{x}) = \sigma_f^2 k(\mathbf{0}) = \sigma_f^2\,, \end{aligned} i.e., the variance of the underlying function at $\mathbf x$ (and therefore a statistical statement about its amplitude) is directly given by the signal variance $\sigma_f^2$. More specifically, the amplitude is related to the square root of the signal variance: We expect most function values to be within the $2\sqrt{\sigma_f^2}$ range around $0$. Figure 3 illustrates that only the scaling of the amplitude of the function changes, depending on the signal variance.

4.3 Noise variance

### Noise variance

The noise variance $\sigma_n^2$ is not a direct parameter of the GP, but a parameter of the likelihood function. It indicates how much measurement noise the observations contain. In the basic model we consider in this article, the function values $f(\mathbf{x})$ are corrupted by i.i.d. Gaussian noise with mean $0$ and variance $\sigma_n^2$. If the measurement noise is $0$, the posterior mean function of the GP will pass through all observations. This may require a complex function with many changes in direction. If the measurement noise is big, the posterior mean function will not necessarily pass through the observations. Instead, it smoothes out the noisy observations.

Figure 4 shows samples from a GP prior with a fixed lengthscale and signal variance but varying noise variance. The greater the noise variance, the noisier the observations (colored) of the underlying sampled function (black, solid). We fixed the random seed to allow for an easier comparison of the noise effect on the GP samples. For greater values of $\sigma_n$, the noise dominates the signal of the underlying function.

5 Hyperparameter optimization

## Hyperparameter optimization

When training the Gaussian process, the standard procedure is to maximize the marginal likelihood $p(\mathbf{y}|\mathbf{X},\mathbf{\theta})$ with respect to the GP hyperparameters $\mathbf{\theta}$. The marginal likelihood is non-convex with potentially multiple local optima. Therefore, we may end up in (bad) local optima when we choose a gradient-based optimization method (e.g., conjugate gradients or BFGS). This happens more often in the small-data regime, where data can be interpreted in multiple ways. Figure 5 shows an example of the log-marginal likelihood with three different local optima. The figure shows the log-marginal likelihood contour as a function of the lengthscale and noise parameters. For every pair $(l, \sigma_n)$ we found the best signal variance parameter (not displayed).

With an increasing size of the training dataset, the marginal likelihood tends to have a single optimum (possibly with huge plateaus).

In the following, we provide some practical guidelines for initializing the hyperparameters, which may be useful to find good local optima.

5.1 Hyperparameter initiatlization

### Initialization of the hyperparameters: useful heuristics

Earlier, we interpreted the standard hyperparameters of a GP: the signal variance and the lengthscales for the covariance function, and the noise variance for the (Gaussian) likelihood. In order to initialize these parameters to reasonable values when we optimize the marginal likelihood, we need to align them with what we know about the data, either empirically or using prior knowledge. Assume, we have training inputs $\mathbf{X}$ and training targets $\mathbf{y}$. We will see that the signal and noise variances can be initialized using statistics of the training targets, whereas the lengthscale parameters can be initialized using statistics of the training inputs.

In our standard model, where we assume i.i.d. Gaussian noise and a prior mean function $m\equiv 0$, the variability in the observations $y_i = f(\mathbf{x}_i) + \epsilon,~\epsilon\sim\mathcal N(0,\sigma_n^2)$, has two sources: A structured fluctation due to the function $f$ and a purely random component due to the independent measurement noise. The corresponding contributions to the variance of the observations are given by the signal variance $\sigma_f^2$ and the noise variance $\sigma_n^2$, respectively: \text{var}[y] = \sigma_f^2 + \sigma_n^2\,. Note that the signal variance and the noise variance trade each other off if we want to find good heuristics to initialize both parameters: Since $\text{var}[y] = \sigma_f^2 + \sigma_n^2$ a large signal variance should imply a small noise variance and vice versa.There is a geometric interpretation of this variance decomposition: For a geometric interpretation, it is useful to work in vector spaces and to define an inner product, which will allow us to compute lengths and angles between vectors. We can consider the space of random variables a vector space, which we can equip with an inner product $\langle\cdot, \cdot\rangle$, which is a symmetric, positive definite bilinear form. Let us choose the inner product as $\langle \mathbf a, \mathbf b\rangle = \text{cov}[\mathbf a, \mathbf b]$. Then, the squared norm is given as $\|\mathbf a|\|^2 = \langle \mathbf a,\mathbf a\rangle = \text{var}[\mathbf a]$, i.e., we can interpret the standard deviation of $\mathbf{x}$ as the length of $\mathbf{x}$. Furthermore, the angle $\alpha$ between two random variables (which are vectors in this vector space) $\mathbf a, \mathbf b$ is determined via $\cos(\alpha) = \langle\mathbf a, \mathbf b\rangle/\sqrt{\langle \mathbf a, \mathbf a\rangle\langle \mathbf b, \mathbf b \rangle}$). For uncorrelated random variables $\text{cov}[\mathbf a, \mathbf b] = 0 = \langle \mathbf a, \mathbf b\rangle$, such that the geometric angle between them is $90^\circ$, i.e., $\mathbf a, \mathbf b$ are orthogonal. We obtain the length $\|\mathbf a + \mathbf b\|$ therefore as $\|\mathbf a\|^2 + \|\mathbf b\|^2 + 2\langle\mathbf a, \mathbf b\rangle = \text{var}[\mathbf a] + \text{var}[\mathbf b] + 2\text{cov}[\mathbf a, \mathbf b] = \text{var}[\mathbf a] + \text{var}[\mathbf b]$. In the GP regression case where $\text{var}[y] = \text{var}[f(\mathbf x)] + \text{var}[\epsilon] = \sigma_f^2 + \sigma_n^2$, we can therefore employ the same geometric interpretation:

Due to the independence of the signal variance $\sigma_f^2$ and the noise variance $\sigma_n^2$, their corresponding vectors are orthogonal. Using the Pythagoraen theorem, we obtain $\text{var}[y] = \sigma_f^2 + \sigma_n^2$ from a geometric perspective.

Prior knowledge about measurement noise or signal variance. The noise variance embodies the amount of measurement noise we expect in the observations $\mathbf{y}$. Reasonable (initial) values would be in the range $\big[0, \text{var}[\mathbf y]\big]$. Sometimes, we have some prior knowledge about the measurement noise (e.g., if we use sensors, such as cameras or lasers where we can find these values in the technical specification), we should set the measurement noise to these values, and the signal variance to $\sigma_f^2 \approx \text{var}[\mathbf{y}] - \sigma_n^2$.

Sometimes, we know something about the amplitude of the function we wish to model (e.g., if we consider a sinusoidal function ranging between -1 and +1, the amplitude is 2). In these cases, it makes sense to initialize the signal variance, such that $\sqrt{\sigma_f^2}\approx \frac{\text{amplitude}}{2}$ and $\sigma_n^2 = \text{var}[\mathbf{y}] -\sigma_f^2$. Since the function values are jointly Gaussian distributed in a GP, we would expect that the observed function values are reasonably well covered by this variance.

Unknown signal and noise variance If we know nothing specific about the noise variance or the signal variance, we can still make some practical assumptions: From a practical point of view, there is not much use of attempting to model a function where the noise variance is greater than the signal variance (the signal-to-noise ratio would be smaller than 1). Therefore, we should initialize the signal variance to a value greater than the noise variance: $\sigma_f^2 > \sigma_n^2$.

A reasonable intialization that works well in practice is to set the signal variance to the empirical variance of the observed function values, and the noise variance to a smaller value: \begin{aligned} \sigma_f^2 &\approx \text{var}[\mathbf{y}]\\ \sqrt{\sigma_n^2} &\approx \frac{\sqrt{\sigma_f^2}}{\kappa}\,,\quad \kappa \in[2,100]\,. \end{aligned} Note that the signal-to-noise ratio is the fraction $\sqrt{\frac{\sigma_f^2}{\sigma_n^2}}$, i.e., we would a priori consider signal-to-noise ratios of 2–100 in the above setting where $\kappa\in[2,100]$. This means we expect the signal in the data to be 2–100 times stronger than the noise.

Lengthscales The lengthscale in stationary covariance functions determines how correlated two function values are as a function of the corresponding inputs (scaled by $1/l$). Therefore, a useful heuristic is to set the lengthscale parameters by looking at the statistics of the training inputs $\mathbf{X}$.

Local optima are the largest problem that prevent good lengthscales from being selected through gradient-based optimisation. Generally, we can observe two different types of local optima:

• Long lengthscale, large noise. Often the lengthscale is so long that the prior only allows nearly linear functions in the posterior. As a consequence, a large amount of noise is required to account for the residuals, leading to a small signal-to-noise ratio. This looks like underfitting, as non-linearities in the data are modelled as noise instead of being learned as part of the function.
• Short lengthscale, low noise. Short lengthscales allow the posterior mean to fit to small variations in the data. Often such solutions are accompanied by small noise, and therefore a high signal-to-noise ratio. Such solutions look like they overfit, since the means fit the data by making drastic and fast changes, while generalizing poorly. However, the short lengthscale also prevents the predictive error bars from being small, so all predictions will be made with high uncertainty. In the probabilistic sense, this also looks like underfitting.

Which optimum we end up in, depends on the initialization of our lengthscale as we are likely to end up in a local optimum nearest to our initial choice. In both cases, the optimizer is more likely to get stuck in a local optimum if the situations are a somewhat plausible explanations of the data. In practice, it is harder to get out of a long lengthscale situation since the optimizer often struggles to get beyond the (typically) huge plateau that is typical for very long lengthscales: Assume that our training inputs are in the range [0,1]. From a modeling perspective it does not make a significant difference having lengthscales of 10 or 100: In both cases, the GP effectively describes a linear model. This, however, also means that there is nearly no gradient signal that encourages an initial lengthscale of 100 to be reduced.

When we initialize the lengthscales, we want to give the GP a reasonable learning signal to adjust the lengthscales during optimization. Remembering that the lengthscales determine how far we have to travel from $\mathbf{x}$ to $\mathbf{x}^\prime$ to make $f(\mathbf{x})$ and $f(\mathbf{x}^\prime)$ effectively independent, we can use the variance of the training inputs as a simple guideline: Since $\sqrt{\text{var}[\mathbf{X}]}$the average squared deviation from the mean of the input data. determines the "spread" of the data, it makes sense to initialize the lengthscale $l$ to a value that is somewhat shorter. A heuristic that works quite well in practice is

where $\lambda\approx 1$ would favor more linear functions, whereas $\lambda\approx 10$ would give the model some more flexibility to have extrema within the input range. Figure 7 gives a few examples of samples from a GP prior where the lengthscale parameter of the GP prior is set to a multiple of the standard deviation of the training inputs $\mathbf{X}$, whose locations are indicated by the bars at the bottom.

An alternative heuristic is to compute the distances $\|\mathbf x_n - \mathbf{\bar X}\|$ of the training inputs $\mathbf x_n$, $n = 1, \ldots, N$, from the mean $\mathbf{\bar X}$ of the training inputs and take the median distance as a guideline for setting the lengthscale parameter. This heuristic would be less prone to outliers than the mean.

5.2 Remarks

### Remarks related to hyperparameter optimization

• If possible, re-start the hyperparameter optimization from randomly chosen initializations (random re-starts). Take the hyperparameter setting that leads to the best marginal likelihood value.
• Instead of using a gradient-based optimization method for finding a good set of hyperparameters, we could use a global optimization method, e.g., Bayesian optimization . Bayesian optimization can be applied to learning GP hyperparameters since the number of hyperparameters is typically small (less than 10), and, therefore, in a regime where Bayesian optimization works well.
• The characteristics of overfitting in GPs (as mentioned earlier as a consequence of too short lengthscales) are different from overfitting in more common maximum likelihood contexts. Usually, overfitting appears as predictions that are both incorrect and very certain. Choosing a lengthscale that is too short will give a mean that is wrong, but also error bars that grow quickly away from the data—the model becomes too uncertain!
• To sidestep the issue of optimizing hyperparameters completely, they can be integrated out. However, this cannot be done analytically, and we have to resort to MCMC methods. This will have the largest practical impact if there are large regions in the space of hyperparameters which have similar marginal likelihoods.
• A third way of dealing with hyperparameters has been proposed by Wagberg et al. (2017) where the authors train a GP by optimizing the marginal likelihood and apply a deterministic correction term at prediction time to account for the fact that the hyperparameter posterior is not a delta function. The proposed correction term affects both the predictive mean and variance and is based on a local approximation of the GP mean function.
6 Practical tips

## Practical tips

In the following, we use our hyper-parameter insights and provide practical tips that we find useful when working with Gaussian processes.

6.1 Log-transformation

### Log-transformation

We typically perform a log-transformation for reasons of numerical stability: For training the GP we want to maximize the marginal likelihood $p(\mathbf{y}|\mathbf{X},\mathbf{\theta})$. It is easy to obtain very small (or high) marginal likelihood values that we cannot appropriately represent using 64 (or 32) bits. Maximizing the log-marginal likelihood

\log p(\mathbf{y}|\mathbf{X},\mathbf{\theta}) = -\tfrac{1}{2}\mathbf y^T(\mathbf K + \sigma_n^2\mathbf I)^{-1}\mathbf y -\tfrac{1}{2}|\mathbf K + \sigma_n^2\mathbf I| + \text{ const}

possesses the same optimum as the marginal likelihood, but drastically reduces numerical problems related to numerical underflow/overflow.

We can also apply the log-transformation to the model hyperparameters ($\sigma_f^2, \sigma_n^2, l$) to (a) consider an unconstrained optimization problemOtherwise, we would need to ensure that $\sigma_n^2>0,~\sigma_f^2>0$. An alternative transformation for the hyperparameters is to look at square-root parameters instead of log-parameters. The optimization problem is also unconstrained, and the dynamic range is increased compared to the origina; problem formulation. and (b) increase the dynamic range.

6.2 Numerical stability

### Controlling numerical stability

For a numerically stable inversion of $\mathbf{K} + \sigma_n^2\mathbf{I}$ in the log-marginal likelihood and during predictions, we will look at the condition number of this matrix. The condition number gives a bound on how accurate the solution of this inverse is. The condition number is the ratio $\tfrac{\lambda_{\text{max}}}{\lambda_{\text{min}}}$ of the greatest to the smallest eigenvalue of $\mathbf{K} + \sigma_n^2\mathbf{I}$, and large condition numbers are indicators for numerical instability, which can cause (numerical) problems during learning and inference. By exploiting the equivalences

\begin{aligned} \left(\mathbf{K} + \sigma_n^2\mathbf{I}\right)^{-1} &= \left(\sigma_f^2\tilde{\mathbf{K}} + \sigma_n^2\mathbf{I}\right)^{-1} = \left(\sigma_n^2 \big(\tfrac{\sigma_f^2}{\sigma_n^2}\tilde{\mathbf{K}} + \mathbf{I}\big)\right)^{-1}\\ &=\frac{1}{\sigma_n^2}\left(\tfrac{\sigma_f^2}{\sigma_n^2}\tilde{\mathbf{K}} + \mathbf{I}\right)^{-1} \end{aligned}

we can analyze the eigenvalues of $\mathbf K + \sigma_n^2\mathbf I$ as follows: We know that $\tilde{\mathbf{K}}=\tfrac{1}{\sigma_f^2}\mathbf K$ is symmetric, positive semi-definite, i.e., the eigenvalues must be non-negative. Since $\tilde{\mathbf{K}} = \tfrac{1}{\sigma_f^2}\mathbf{K}$, its diagonal values are $\tilde K_{ii} = 1$. Exploiting the Gershgorin circle theorem we directly bound the eigenvalues of $\tilde{\mathbf{K}}\in \mathbb{R}^{N\times N}$ to the interval $[0,N]$, where $N$ is the number of training data points. Therefore, the eigenvalues of $\tilde{\mathbf{K}} + \mathbf{I}$ lie in the interval $[1,N+1]$, and the eigenvalues of $\tfrac{\sigma_f^2}{\sigma_n^2}\tilde{\mathbf{K}} + \mathbf{I}$ lie in $[1,\tfrac{N\sigma_f^2}{\sigma_n^2}+1]$. Huge condition numbers (and, therefore, the risk of numerical instability) appear when the smallest eigenvalue is tiny and/or the greatest eigenvalue is huge.

We can find an upper bound for the condition number by considering the most extreme case, for which $\lambda_{\text{min}}=1,~\lambda_{\text{max}} = \tfrac{N\sigma_f^2}{\sigma_n^2}+1$.We will give an example below. Defining $\alpha=\tfrac{\sigma_f}{\sigma_n}$ as a shorthand notation for the signal-to-noise ratio, we obtain the condition number

\begin{aligned} \frac{\lambda_{\text{max}}}{\lambda_{\text{min}}} = \frac{N\alpha^2+1}{1} = N\alpha^2+1\,. \end{aligned}

We make two observations:

• The condition number grows linearly with the number $N$ of data points.
• The condition number grows quadratically with the signal-to-noise ratio $\tfrac{\sigma_f}{\sigma_n}$.

From these observations, we can directly see that the signal-to-noise ratio plays a significant role when it comes to numerical stability: High signal-to-noise ratios make the matrix $\mathbf{K} + \sigma_n^2\mathbf{I}$ ill-conditioned. This result is to some degree odd since we are generally interested in "clean" data. However, for reasons of numerical stability noise-free data should be avoided.

6.2.1

#### Extreme eigenvalues appear when function values are strongly correlated

In practice, we encounter extreme eigenvalues $\lambda_{\text{max}},~\lambda_{\text{min}}$ (and therefore unfavorable condition numbers) when function values $f_1, f_2$ corresponding to training inputs $\mathbf x_1, \mathbf x_2$ are strongly correlated. Strong correlation between function values will result in near-identical corresponding rows and columns in the kernel matrix $\mathbf K$, such that the kernel matrix becomes close to singular.

In the standard model we consider here, we obtain an eigenvalue of close to $0$ when function values $f_1, f_2$ are strongly correlated, i.e., the corresponding input locations $\mathbf{x}_1, \mathbf{x}_2$ are close to each other. More precisely, the covariance

cov[f(\mathbf{x}_1), f(\mathbf{x}_2)] = \sigma_f^2 \tilde k(\|\mathbf{x}_1 - \mathbf{x}_2\|/l)

is maximized (assuming fixed hyperparameters) when $\mathbf{x}_1\approx \mathbf{x}_2$, where the lengthscale parameter $l$ indicates how close $\mathbf{x}_1$ and $\mathbf{x}_2$ need to be to each other.

Let us consider an example and investigate the matrix $\tilde{\mathbf{K}}$ by setting $\sigma_f=1$, such that we can use the correlation plot from above for illustration purposes: To obtain strong correlations of two function values, for short lengthscales, the two corresponding input locations need to be very close to each other, whereas for long lengthscales, the input locations can be far from each other. If the correlation in the figure above is close to $1$, the distance between the two inputs is sufficiently small to make the kernel matrix singular (rank deficient). This will result in an eigenvalue $0$, i.e., $\tilde{\mathbf K}$ is not invertible. An eigenvalue $N$ is obtained if all function values are strongly correlated, i.e., $\tilde{\mathbf{K}}\approx \mathbf{1}$ where $\mathbf{1}$ is an $N\times N$ matrix of $1$s. In this "flat" matrix (with $rank(\tilde{\mathbf K})=1$) the eigenvalue $0$ would appear with multliplicity $N-1$ and the eigenvalue $N$ would appear once:

\text{trace}(\tilde{\mathbf{K}}) = N =\sum_{n=1}^N \lambda_n

where $\lambda_n$ are the eigenvalues of $\tilde{\mathbf{K}}$. Therefore, the eigenvalues of $\tfrac{\sigma_f^2}{\sigma_n^2}\tilde{\mathbf K}+\mathbf I$ are 1 ($N-1$ times) and $\tfrac{N\sigma_f^2}{\sigma_n^2}+1$.

6.2.2

#### Controlling the condition numbers

Useful heuristics for controlling the condition number of $\mathbf K + \sigma_n^2\mathbf I$ include

• adding an explicit penalty on the signal-to-noise ratio during training

Adding a jitter term. Numerical stability can be increased by adding a jitter term $\epsilon^2\mathbf I$ to $\mathbf K + \sigma_n^2 \mathbf I$. In some sense, this term adds a notion of "noise" without making the training targets $\mathbf y$ noisier. By adding the jitter term to the diagonal of the kernel matrix, we algorithmically restrict the model class to models that have a minimum of $\epsilon^2$ noise variance. Alternatively, we can instead directly add Gaussian noise (e.g., with standard deviation $\epsilon = \sigma_f/100$) to the training targets $\mathbf y$. When optimizing the hyperparameters the noise variance $\sigma_n^2$ will increase by $\epsilon^2$.

Penalizing the signal-to-noise ratio. When training the GP by maximizing the log-marginal likelihood, we can add a penalty term to the log-marginal likelihood that discourages extreme signal-to-noise ratios. For this, we need to define an "acceptable" signal-to-noise ratio $\text{maxSNR}$ (e.g., 10,000), such that our training objective becomes

\log p(\mathbf{y}|\mathbf{X}, \mathbf{\theta}) - \left(\frac{\log(\sigma_f/\sigma_n)}{\log(\text{maxSNR})}\right)^{50}

The effect of the penalty on the signal-to-noise ratio is insignificant if the signal-to-noise ratio $\sigma_f/\sigma_n$ is smaller than our threshold $\text{maxSNR}$. Raising this penalty to the power of 50 ensures that there is a strong penalty on exceeding $\text{maxSNR}$. This kind of penalty on the signal-to-noise ratio is used in .

Nevertheless, it can happen that the signal-to-noise ratio exceeds $\text{maxSNR}$, despite the fact that this is strongly discouraged. When the trained hyperparameters lead to an extreme signal-to-noise ratio $\sigma_f/\sigma_n > \text{maxSNR}$, this is a strong indicator that the data is very "clean", i.e., noise-free. In this case, we could artificially increase the noise in the data or add an additional jitter term to $\sigma_n^2\mathbf I$ to guarantee numerical stability. It is therefore useful to print the signal-to-noise ratio after training for inspection purposes.

6.2.3

#### The influence of controlling the condition number on the model

Above, we saw that the condition number can be controlled by essentially modifying the amount of noise in the observations. It may seem like this is an arbitrary and unacceptable degradation of the model's performance. When evaluating predictive performance, the metric of log predictive density in particular can take a huge penalty. When predicting with near certainty (and being correct), the log predictive density can be arbitrarily high. Adding jitter will strongly bring these numbers down, as the predictive variance will be lower bounded by the jitter. However, it can be argued that this metric is not very meaningful in this particular regime: The condition number is only a problem when there is so little noise in the data that we are predicting at an accuracy close to the numerical precision of our hardware. For many practical problems, such a high level of accuracy is not needed. In addition, the root mean squared error will not be affected much.

6.3 Data normalization

### Data normalization

Data normalization as a form of data pre-processing is something we often do not talk about since the GP possesses all the tools to deal with it automatically. However, in practice, data normalization can make our lives significantly easier.

6.3.1

#### Normalizing the inputs

The GP does not require any input normalization, but it can make sense to do so for numerical reasons. For stationary covariance functions, it does not matter whether the mean is subtracted or not since

\tau = \|\mathbf x_i - \bar{\mathbf X} - (\mathbf x_j - \bar{\mathbf X})\| = \|\mathbf x_i - \mathbf x_j\|\,.

There can be reasons to subtract the mean $\bar{\mathbf X}$ of the training inputs if the data lies far away from $0$. However, if this causes numerical problems, we will most likely run into other (numerical) issues before this one becomes relevant.

It can make sense to standardize the data, i.e., subtract the mean and divide by the standard deviation of the training inputs, i.e., our inputs are given by

\tilde{\mathbf x} = \frac{\mathbf x - \bar{\mathbf X}}{\sqrt{\text{var}[\mathbf X]}}\,,

such that the input data possesses mean $0$ and standard deviation $1$ in each input dimension. Note that the lengthscale parameter can achieve the same result. Since only the term $\tau/l$ appears in the stationary covariance functions, and we set $l = \sqrt{\text{var}[\mathbf X]}$, the same result is obtained:

\|\tilde{\mathbf x}_i - \tilde{\mathbf x}_j\| = \left\| \frac{\mathbf x_i - \bar{\mathbf X}}{\sqrt{\text{var}[\mathbf X]}} - \frac{\mathbf x_j - \bar{\mathbf X}}{\sqrt{\text{var}[\mathbf X]}}\right\| = \frac{\|\mathbf x_i - \mathbf x_j\|}{\sqrt{\text{var}[\mathbf X]}} = \frac{\tau_{ij}}{l}

for $\tau_{ij} = \|\mathbf x_i - \mathbf x_j\|$ and $l = \sqrt{\text{var}[\mathbf X]}$. However, if the units of the input features are in a way that very small or great numbers appearConsider input features to be geo-locations of cities in Africa measured in millimeters from $(0^\circ,0^\circ)$ instead of km or CO2 emissions of a car measured in Giga tons (instead of mg)., we would be dividing by very small/great numbers, which can lead to numerical problems.

Although the standardization removes the interpretability of the dataStandardization removes units from the data., it is possible to recover their original meaning (and unit) by the inverse operation

\mathbf x = \tilde{\mathbf x}\sqrt{\text{var}[\mathbf X]} + \bar{\mathbf X}\,.

Similarly, we will need to re-scale the lengthscales to obtain their meaning in the original data space. As long as the input features contain numbers of approximately the same (reasonable) order of magnitude, input normalization is not necessary.

6.3.2

#### Normalizing the targets

Normalization of the targets makes modeling the data easier. Even only subtracting the mean of the training targets from the (training and test) targets makes a big difference: In the standard model, we use a prior mean function $m(\cdot) \equiv 0$. Above we argued that this makes sense if we have no prior knowledge about the data, such that a symmetry argument makes a zero prior mean function a good choice. This prior also would not bias our model in a specific way.

However, we do have access to training targets, and it is often the case that the training targets are not spread around $0$. For instance, if we wish to model Q-values in a reinforcement learning setting, a value of $0$ has a specific meaning.In the case where observed rewards are all negative (or costs are all positive), a Q-value of $0$ would encode that the corresponding state-action pair is very favorable, which will bias a Q-learning agent to aggressive exploration. A different example is in robotics where we may wish to model the robot's dynamics. However, the $0$ state could mean something very specific, e.g., the default position of the robot that it goes to when it boots.

To avoid model bias toward these specific "meaningful zeros", subtracting the mean of the training targets makes sense and removes the bias from the data. We would then obtain

\tilde{y} = y - \bar{\mathbf y}\,,

where $\bar{\mathbf y}$ is the mean of the training targets. An alternative way to deal with this problem is to modify the covariance function and use a sum of covariance function: our original kernel plus a constant kernel that automatically learns the necessary bias/offset in the training targets. Although this approach is an elegant solution and allows the GP to infer necessary information from available data, it does require to optimize an additional hyperparameter that takes care of the offset. In practice, it is often easier to use information from the training data directly and ignore the constant kernel.

Additional to the "centering" of the training targets, it is also possible to divide by the standard deviation of the training targets, such that

\tilde{y} = \frac{y - \bar{\mathbf y}}{\sqrt{\text{var}[\mathbf y]}}\,.

This standardization step can make sense if the observed function values are extreme. For example, if we measured distances between cities in millimeters instead of kilometers the latent function describing these distances would need to describe function values in the billions/trillions. For example, the distance from London to Cologne is approximately 500 km = 500,000,000 mm. Theoretically, this is not a problem, but in practice (even after subtracting the mean of the training data) we will run into problems of numerical stability and numerical precision.

7 Conclusion

## Conclusion

We provided an intuitive understanding of the meaning of three typical hyperparameters in Gaussian process models: the signal variance, the lengthscale, and the noise variance parameter. We used this intuition to suggest heuristics to initialize the optimization of the hyperparameters when training the GP. Moreover, we shed some light on the reasons for numerical instability, which frequently occurs in Gaussian processes. We identified the signal-to-noise ratio as a quantity that controls numerical stability and proposed guidelines to control numerical stability.

Generally, it is useful to use available data as much as possible to guide the Gaussian process (e.g., initialization of the hyperparameters when training the model). Since the Gaussian process relies on numerical precision, it is also advisable to pay attention to the scale of the data (inputs and targets): Very small or large values are generally sources of numerical problems, and data normalization can be a helpful tool.