Gaussian Processes and Kernel Methods

Gaussian processes are non-parametric distributions useful for doing Bayesian inference and learning on unknown functions. They can be used for non-linear regression, time-series modelling, classification, and many other problems.

Archipelago: nonparametric Bayesian semi-supervised learning

R. Adams, Zoubin Ghahramani, June 2009. (In 26th International Conference on Machine Learning). Edited by Léon Bottou, Michael Littman. Montréal, QC, Canada. Omnipress.

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Semi-supervised learning (SSL), is classification where additional unlabeled data can be used to improve accuracy. Generative approaches are appealing in this situation, as a model of the data’s probability density can assist in identifying clusters. Nonparametric Bayesian methods, while ideal in theory due to their principled motivations, have been difficult to apply to SSL in practice. We present a nonparametric Bayesian method that uses Gaussian processes for the generative model, avoiding many of the problems associated with Dirichlet process mixture models. Our model is fully generative and we take advantage of recent advances in Markov chain Monte Carlo algorithms to provide a practical inference method. Our method compares favorably to competing approaches on synthetic and real-world multi-class data.

Comment: This paper was awarded Honourable Mention for Best Paper at ICML 2009.

Deep kernel processes

Laurence Aitchison, Adam X. Yang, Sebastian W. Ober, 2021. (In 38th International Conference on Machine Learning).

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We define deep kernel processes in which positive definite Gram matrices are progressively transformed by nonlinear kernel functions and by sampling from (inverse) Wishart distributions. Remarkably, we find that deep Gaussian processes (DGPs), Bayesian neural networks (BNNs), infinite BNNs, and infinite BNNs with bottlenecks can all be written as deep kernel processes. For DGPs the equivalence arises because the Gram matrix formed by the inner product of features is Wishart distributed, and as we show, standard isotropic kernels can be written entirely in terms of this Gram matrix — we do not need knowledge of the underlying features. We define a tractable deep kernel process, the deep inverse Wishart process, and give a doubly-stochastic inducing-point variational inference scheme that operates on the Gram matrices, not on the features, as in DGPs. We show that the deep inverse Wishart process gives superior performance to DGPs and infinite BNNs on fully-connected baselines.

Perfusion Quantification using Gaussian Process Deconvolution

Irene K. Andersen, Anna Szymkowiak, Carl Edward Rasmussen, L. G. Hanson, J. R. Marstrand, H. B. W. Larsson, Lars Kai Hansen, 2002. (Magnetic Resonance in Medicine). DOI: 10.1002/mrm.10213.

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The quantification of perfusion using dynamic susceptibility contrast MR imaging requires deconvolution to obtain the residual impulse-response function (IRF). Here, a method using a Gaussian process for deconvolution, GPD, is proposed. The fact that the IRF is smooth is incorporated as a constraint in the method. The GPD method, which automatically estimates the noise level in each voxel, has the advantage that model parameters are optimized automatically. The GPD is compared to singular value decomposition (SVD) using a common threshold for the singular values and to SVD using a threshold optimized according to the noise level in each voxel. The comparison is carried out using artificial data as well as using data from healthy volunteers. It is shown that GPD is comparable to SVD variable optimized threshold when determining the maximum of the IRF, which is directly related to the perfusion. GPD provides a better estimate of the entire IRF. As the signal to noise ratio increases or the time resolution of the measurements increases, GPD is shown to be superior to SVD. This is also found for large distribution volumes.

Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients

Artem Artemev, David R. Burt, Mark van der Wilk, 2021. (In 38th International Conference on Machine Learning).

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We propose a lower bound on the log marginal likelihood of Gaussian process regression models that can be computed without matrix factorisation of the full kernel matrix. We show that approximate maximum likelihood learning of model parameters by maximising our lower bound retains many benefits of the sparse variational approach while reducing the bias introduced into hyperparameter learning. The basis of our bound is a more careful analysis of the log-determinant term appearing in the log marginal likelihood, as well as using the method of conjugate gradients to derive tight lower bounds on the term involving a quadratic form. Our approach is a step forward in unifying methods relying on lower bound maximisation (e.g. variational methods) and iterative approaches based on conjugate gradients for training Gaussian processes. In experiments, we show improved predictive performance with our model for a comparable amount of training time compared to other conjugate gradient based approaches.

Sparse Gaussian process variational autoencoders

Matthew Ashman, Jonny So, Will Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner, 2020.

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Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generative models (GP-DGMs), which employ GP priors over the latent variables of DGMs. Existing approaches for performing inference in GP-DGMs do not support sparse GP approximations based on inducing points, which are essential for the computational efficiency of GPs, nor do they handle missing data – a natural occurrence in many spatio-temporal datasets – in a principled manner. We address these shortcomings with the development of the sparse Gaussian process variational autoencoder (SGP-VAE), characterised by the use of partial inference networks for parameterising sparse GP approximations. Leveraging the benefits of amortised variational inference, the SGP-VAE enables inference in multi-output sparse GPs on previously unobserved data with no additional training. The SGP-VAE is evaluated in a variety of experiments where it outperforms alternative approaches including multi-output GPs and structured VAEs.

Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the Compact Case

Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy, 2022. (arXiv).

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Gaussian processes are arguably the most important model class in spatial statistics. They encode prior information about the modeled function and can be used for exact or approximate Bayesian inference. In many applications, particularly in physical sciences and engineering, but also in areas such as geostatistics and neuroscience, invariance to symmetries is one of the most fundamental forms of prior information one can consider. The invariance of a Gaussian process’ covariance to such symmetries gives rise to the most natural generalization of the concept of stationarity to such spaces. In this work, we develop constructive and practical techniques for building stationary Gaussian processes on a very large class of non-Euclidean spaces arising in the context of symmetries. Our techniques make it possible to (i) calculate covariance kernels and (ii) sample from prior and posterior Gaussian processes defined on such spaces, both in a practical manner. This work is split into two parts, each involving different technical considerations: part I studies compact spaces, while part II studies non-compact spaces possessing certain structure. Our contributions make the non-Euclidean Gaussian process models we study compatible with well-understood computational techniques available in standard Gaussian process software packages, thereby making them accessible to practitioners.

The Mondrian Kernel

Matej Balog, Balaji Lakshminarayanan, Zoubin Ghahramani, Daniel M. Roy, Yee Whye Teh, June 2016. (In 32nd Conference on Uncertainty in Artificial Intelligence). Jersey City, New Jersey, USA.

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We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is suitable for both batch and online learning, and admits a fast kernel-width-selection procedure as the random features can be re-used efficiently for all kernel widths. The features are constructed by sampling trees via a Mondrian process [Roy and Teh, 2009], and we highlight the connection to Mondrian forests [Lakshminarayanan et al., 2014], where trees are also sampled via a Mondrian process, but fit independently. This link provides a new insight into the relationship between kernel methods and random forests.

Comment: [Supplementary Material] [arXiv] [Poster] [Slides] [Code]

Differentially Private Database Release via Kernel Mean Embeddings

Matej Balog, Ilya Tolstikhin, Bernhard Schölkopf, July 2018. (In 35th International Conference on Machine Learning). Stockholm, Sweden.

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We lay theoretical foundations for new database release mechanisms that allow third-parties to construct consistent estimators of population statistics, while ensuring that the privacy of each individual contributing to the database is protected. The proposed framework rests on two main ideas. First, releasing (an estimate of) the kernel mean embedding of the data generating random variable instead of the database itself still allows third-parties to construct consistent estimators of a wide class of population statistics. Second, the algorithm can satisfy the definition of differential privacy by basing the released kernel mean embedding on entirely synthetic data points, while controlling accuracy through the metric available in a Reproducing Kernel Hilbert Space. We describe two instantiations of the proposed framework, suitable under different scenarios, and prove theoretical results guaranteeing differential privacy of the resulting algorithms and the consistency of estimators constructed from their outputs.

Comment: [arXiv]

Understanding Probabilistic Sparse Gaussian Process Approximations

Matthias Stephan Bauer, Mark van der Wilk, Carl Edward Rasmussen, 2016. (In Advances in Neural Information Processing Systems 29).

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Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial similarities, these approximations have surprisingly different theoretical properties and behave differently in practice. We thoroughly investigate the two methods for regression both analytically and through illustrative examples, and draw conclusions to guide practical application.

Comment: arXiv

Policy search for learning robot control using sparse data

B. Bischoff, D. Nguyen-Tuong, D. van Hoof, A. McHutchon, Carl Edward Rasmussen, A. Knoll, M. P. Deisenroth, 2014. (In IEEE International Conference on Robotics and Automation). Hong Kong, China. IEEE. DOI: 10.1109/ICRA.2014.6907422.

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In many complex robot applications, such as grasping and manipulation, it is difficult to program desired task solutions beforehand, as robots are within an uncertain and dynamic environment. In such cases, learning tasks from experience can be a useful alternative. To obtain a sound learning and generalization performance, machine learning, especially, reinforcement learning, usually requires sufficient data. However, in cases where only little data is available for learning, due to system constraints and practical issues, reinforcement learning can act suboptimally. In this paper, we investigate how model-based reinforcement learning, in particular the probabilistic inference for learning control method (PILCO), can be tailored to cope with the case of sparse data to speed up learning. The basic idea is to include further prior knowledge into the learning process. As PILCO is built on the probabilistic Gaussian processes framework, additional system knowledge can be incorporated by defining appropriate prior distributions, e.g. a linear mean Gaussian prior. The resulting PILCO formulation remains in closed form and analytically tractable. The proposed approach is evaluated in simulation as well as on a physical robot, the Festo Robotino XT. For the robot evaluation, we employ the approach for learning an object pick-up task. The results show that by including prior knowledge, policy learning can be sped up in presence of sparse data.

Bayesian Structured Prediction Using Gaussian Processes

Sébastien Bratières, Novi Quadrianto, Zoubin Ghahramani, 2013. (arXiv).

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We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M3N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct.

Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications

Sébastien Bratières, Novi Quadrianto, Sebastian Nowozin, Zoubin Ghahramani, 2014. (In 31st International Conference on Machine Learning).

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Structured prediction is an important and well studied problem with many applications across machine learning. GPstruct is a recently proposed structured prediction model that offers appealing properties such as being kernelised, non-parametric, and supporting Bayesian inference (Bratières et al. 2013). The model places a Gaussian process prior over energy functions which describe relationships between input variables and structured output variables. However, the memory demand of GPstruct is quadratic in the number of latent variables and training runtime scales cubically. This prevents GPstruct from being applied to problems involving grid factor graphs, which are prevalent in computer vision and spatial statistics applications. Here we explore a scalable approach to learning GPstruct models based on ensemble learning, with weak learners (predictors) trained on subsets of the latent variables and bootstrap data, which can easily be distributed. We show experiments with 4M latent variables on image segmentation. Our method outperforms widely-used conditional random field models trained with pseudo-likelihood. Moreover, in image segmentation problems it improves over recent state-of-the-art marginal optimisation methods in terms of predictive performance and uncertainty calibration. Finally, it generalises well on all training set sizes.

Scalable Exact Inference in Multi-Output Gaussian Processes

Wessel Bruinsma, Eric Perim, Will Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner, 2020. (In 37th International Conference on Machine Learning). Proceedings of Machine Learning Research.

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Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-temporal modelling. The key problem with MOGPs is their computational scaling O(n^3 p^3), which is cubic in the number of both inputs n (e.g., time points or locations) and outputs p. For this reason, a popular class of MOGPs assumes that the data live around a low-dimensional linear subspace, reducing the complexity to O(n^3 m^3). However, this cost is still cubic in the dimensionality of the subspace m, which is still prohibitively expensive for many applications. We propose the use of a sufficient statistic of the data to accelerate inference and learning in MOGPs with orthogonal bases. The method achieves linear scaling in m in practice, allowing these models to scale to large m without sacrificing significant expressivity or requiring approximation. This advance opens up a wide range of real-world tasks and can be combined with existing GP approximations in a plug-and-play way. We demonstrate the efficacy of the method on various synthetic and real-world data sets.

Modelling Non-Smooth Signals with Complex Spectral Structure

Wessel P. Bruinsma, Martin Tegnér, Richard E. Turner, 2022. (In 25th International Conference on Artificial Intelligence and Statistics).

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The Gaussian Process Convolution Model (GPCM; Tobar et al., 2015a) is a model for signals with complex spectral structure. A significant limitation of the GPCM is that it assumes a rapidly decaying spectrum: it can only model smooth signals. Moreover, inference in the GPCM currently requires (1) a mean-field assumption, resulting in poorly calibrated uncertainties, and (2) a tedious variational optimisation of large covariance matrices. We redesign the GPCM model to induce a richer distribution over the spectrum with relaxed assumptions about smoothness: the Causal Gaussian Process Convolution Model (CGPCM) introduces a causality assumption into the GPCM, and the Rough Gaussian Process Convolution Model (RGPCM) can be interpreted as a Bayesian nonparametric generalisation of the fractional Ornstein–Uhlenbeck process. We also propose a more effective variational inference scheme, going beyond the mean-field assumption: we design a Gibbs sampler which directly samples from the optimal variational solution, circumventing any variational optimisation entirely. The proposed variations of the GPCM are validated in experiments on synthetic and real-world data, showing promising results.

Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Thang D. Bui, Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Yingzhen Li, Richard E. Turner, June 2016. (In 33rd International Conference on Machine Learning). New York, USA.

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Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are nonparametric probabilistic models and as such are arguably more flexible, have a greater capacity to generalise, and provide better calibrated uncertainty estimates than alternative deep models. This paper develops a new approximate Bayesian learning scheme that enables DGPs to be applied to a range of medium to large scale regression problems for the first time. The new method uses an approximate Expectation Propagation procedure and a novel and efficient extension of the probabilistic backpropagation algorithm for learning. We evaluate the new method for non-linear regression on eleven real-world datasets, showing that it always outperforms GP regression and is almost always better than state-of-the-art deterministic and sampling-based approximate inference methods for Bayesian neural networks. As a by-product, this work provides a comprehensive analysis of six approximate Bayesian methods for training neural networks.

Streaming sparse Gaussian process approximations

Thang D. Bui, Cuong V. Nguyen, Richard E. Turner, December 2017. (In Advances in Neural Information Processing Systems 31). Long Beach, California, USA.

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Sparse approximations for Gaussian process models provide a suite of methods that enable these models to be deployed in large data regime and enable analytic intractabilities to be sidestepped. However, the field lacks a principled method to handle streaming data in which the posterior distribution over function values and the hyperparameters are updated in an online fashion. The small number of existing approaches either use suboptimal hand-crafted heuristics for hyperparameter learning, or suffer from catastrophic forgetting or slow updating when new data arrive. This paper develops a new principled framework for deploying Gaussian process probabilistic models in the streaming setting, providing principled methods for learning hyperparameters and optimising pseudo-input locations. The proposed framework is experimentally validated using synthetic and real-world datasets.

Comment: The first two authors contributed equally.

Tree-structured Gaussian Process Approximations

Thang D. Bui, Richard E. Turner, 2014. (In Advances in Neural Information Processing Systems 28). Edited by Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger. Curran Associates, Inc..

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Gaussian process regression can be accelerated by constructing a small pseudo-dataset to summarize the observed data. This idea sits at the heart of many approximation schemes, but such an approach requires the number of pseudo-datapoints to be scaled with the range of the input space if the accuracy of the approximation is to be maintained. This presents problems in time-series settings or in spatial datasets where large numbers of pseudo-datapoints are required since computation typically scales quadratically with the pseudo-dataset size. In this paper we devise an approximation whose complexity grows linearly with the number of pseudo-datapoints. This is achieved by imposing a tree or chain structure on the pseudo-datapoints and calibrating the approximation using a Kullback-Leibler (KL) minimization. Inference and learning can then be performed efficiently using the Gaussian belief propagation algorithm. We demonstrate the validity of our approach on a set of challenging regression tasks including missing data imputation for audio and spatial datasets. We trace out the speed-accuracy trade-off for the new method and show that the frontier dominates those obtained from a large number of existing approximation techniques.

A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation

Thang D. Bui, Josiah Yan, Richard E. Turner, 2017. (Journal of Machine Learning Research).

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Gaussian processes (GPs) are flexible distributions over functions that enable high-level assumptions about unknown functions to be encoded in a parsimonious, flexible and general way. Although elegant, the application of GPs is limited by computational and analytical intractabilities that arise when data are sufficiently numerous or when employing non-Gaussian models. Consequently, a wealth of GP approximation schemes have been developed over the last 15 years to address these key limitations. Many of these schemes employ a small set of pseudo data points to summarise the actual data. In this paper we develop a new pseudo-point approximation framework using Power Expectation Propagation (Power EP) that unifies a large number of these pseudo-point approximations. Unlike much of the previous venerable work in this area, the new framework is built on standard methods for approximate inference (variational free-energy, EP and Power EP methods) rather than employing approximations to the probabilistic generative model itself. In this way all of the approximation is performed at inference time' rather than atmodelling time’, resolving awkward philosophical and empirical questions that trouble previous approaches. Crucially, we demonstrate that the new framework includes new pseudo-point approximation methods that outperform current approaches on regression and classification tasks.

Scalable Approximate Inference and Model Selection in Gaussian Process Regression

David R. Burt, 2022. University of Cambridge, Department of Engineering, Cambridge, UK.

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Models with Gaussian process priors and Gaussian likelihoods are one of only a handful of Bayesian models where inference can be performed without the need for approximation. However, a frequent criticism of these models from practitioners of Bayesian machine learning is that they are challenging to scale to large datasets due to the need to compute a large kernel matrix and perform standard linear-algebraic operations with this matrix. This limitation has driven decades of research in both statistics and machine learning seeking to scale Gaussian process regression models to ever-larger datasets. This thesis builds on this line of research. We focus on the problem of approximate inference and model selection with approximate maximum marginal likelihood as applied to Gaussian process regression. Our discussion is guided by three questions: Does an approximation work on a range of models and datasets? Can you verify that an approximation has worked on a given dataset? Is an approximation easy for a practitioner to use? While we are far from the first to ask these questions, we offer new insights into each question in the context of Gaussian process regression. In the first part of this thesis, we focus on sparse variational Gaussian process regression (Titsias, 2009). We provide new diagnostics for inference with this method that can be used as practical guides for practitioners trying to balance computation and accuracy with this approximation. We then provide an asymptotic analysis that highlights properties of the model and dataset that are sufficient for this approximation to perform reliable inference with a small computational cost. This analysis builds on an approach laid out in Burt (2018), as well as on similar guarantees in the kernel ridge regression literature. In the second part of this thesis, we consider iterative methods, especially the method of conjugate gradients, as applied to Gaussian process regression (Gibbs and MacKay, 1997). We primarily focus on improving the reliability of approximate maximum marginal likelihood when using these approximations. We investigate how the method of conjugate gradients and related approaches can be used to derive bounds on quantities related to the log marginal likelihood. This idea can be used to improve the speed and stability of model selection with these approaches, making them easier to use in practice.

Rates of Convergence for Sparse Variational Gaussian Process Regression

David R Burt, Carl Edward Rasmussen, Mark van der Wilk, 2019. (arXiv).

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Excellent variational approximations to Gaussian process posteriors have been developed which avoid the O(N3) scaling with dataset size N. They reduce the computational cost to O(NM2), with M ≪N being the number of inducing variables, which summarise the process. While the computational cost seems to be linear in N, the true complexity of the algorithm depends on how M must increase to ensure a certain quality of approximation. We address this by characterising the behavior of an upper bound on the KL divergence to the posterior. We show that with high probability the KL divergence can be made arbitrarily small by growing M more slowly than N. A particular case of interest is that for regression with normally distributed inputs in D-dimensions with the popular Squared Exponential kernel, M=O(logDN) is sufficient. Our results show that as datasets grow, Gaussian process posteriors can truly be approximated cheaply, and provide a concrete rule for how to increase M in continual learning scenarios.

Convergence of Sparse Variational Inference in Gaussian Processes Regression

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk, 2020. (Journal of Machine Learning Research).

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Gaussian processes are distributions over functions that are versatile and mathematically convenient priors in Bayesian modelling. However, their use is often impeded for data with large numbers of observations, N, due to the cubic (in N) cost of matrix operations used in exact inference. Many solutions have been proposed that rely on M 2). While the computational cost appears linear in N, the true complexity depends on how M must scale with N to ensure a certain quality of the approximation. In this work, we investigate upper and lower bounds on how M needs to grow with N to ensure high quality approximations. We show that we can make the KL-divergence between the approximate model and the exact posterior arbitrarily small for a Gaussian-noise regression model with M D) suffice and a method with an overall computational cost of O(N(log N)2D(log log N)2) can be used to perform inference.

Manifold Gaussian Processes for Regression

Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth, 2016. (In International Joint Conference on Neural Networks).

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Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness assumptions on the structure of the function to be modeled. To model complex and nondifferentiable functions, these smoothness assumptions are often too restrictive. One way to alleviate this limitation is to find a different representation of the data by introducing a feature space. This feature space is often learned in an unsupervised way, which might lead to data representations that are not useful for the overall regression task. In this paper, we propose Manifold Gaussian Processes, a novel supervised method that jointly learns a transformation of the data into a feature space and a GP regression from the feature space to observed space. The Manifold GP is a full GP and allows to learn data representations, which are useful for the overall regression task. As a proof-of-concept, we evaluate our approach on complex non-smooth functions where standard GPs perform poorly, such as step functions and robotics tasks with contacts.

Lazily Adapted Constant Kinky Inference for non-parametric regression and model-reference adaptive control

Jan-Peter Calliess, Stephen J. Roberts, Carl Edward Rasmussen, Jan Maciejowski, 2020. (Automatica). DOI: 10.1016/j.automatica.2020.109216.

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Techniques known as Nonlinear Set Membership prediction or Lipschitz Interpolation are approaches to supervised machine learning that utilise presupposed Lipschitz properties to perform inference over unobserved function values. Provided a bound on the true best Lipschitz constant of the target function is known a priori, they offer convergence guarantees, as well as bounds around the predictions. Considering a more general setting that builds on Lipschitz continuity, we propose an online method for estimating the Lipschitz constant online from function value observations that are possibly corrupted by bounded noise. Utilising this as a data-dependent hyper-parameter gives rise to a nonparametric machine learning method, for which we establish strong universal approximation guarantees. That is, we show that our prediction rule can learn any continuous function on compact support in the limit of increasingly dense data, up to a worst-case error that can be bounded by the level of observational error. We also consider applications of our nonparametric regression method to learning-based control. For a class of discrete-time settings, we establish convergence guarantees on the closed-loop tracking error of our online learning-based controllers. To provide evidence that our method can be beneficial not only in theory but also in practice, we apply it in the context of nonparametric model-reference adaptive control (MRAC). Across a range of simulated aircraft roll-dynamics and performance metrics our approach outperforms recently proposed alternatives that were based on Gaussian processes and RBF-neural networks.

Influence of heart rate on the BOLD signal: the cardiac response function

C. Chang, J. P. Cunningham, G. Glover, 2009. (NeuroImage).

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It has previously been shown that low-frequency fluctuations in both respiratory volume and cardiac rate can induce changes in the blood-oxygen level dependent (BOLD) signal. Such physiological noise can obscure the detection of neural activation using fMRI, and it is therefore important to model and remove the effects of this noise. While a hemodynamic response function relating respiratory variation (RV) and the BOLD signal has been described, no such mapping for heart rate (HR) has been proposed. In the current study, the effects of RV and HR are simultaneously deconvolved from resting state fMRI. It is demonstrated that a convolution model including RV and HR can explain significantly more variance in gray matter BOLD signal than a model that includes RV alone, and an average HR response function is proposed that well characterizes our subject population. It is observed that the voxel-wise morphology of the deconvolved RV responses is preserved when HR is included in the model, and that its form is adequately modeled by Birn et al.’s previously described respiration response function. Furthermore, it is shown that modeling out RV and HR can significantly alter functional connectivity maps of the default-mode network.

Understanding Local Linearisation in Variational Gaussian Process State Space Models

Talay M Cheema, 2021. (In Time Series Workshop at the 38th International Conference on Machine Learning).

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We describe variational inference approaches in Gaussian process state space models in terms of local linearisations of the approximate posterior function. Most previous approaches have either assumed independence between the posterior dynamics and latent states (the mean-field (MF) approximation), or optimised free parameters for both, leading to limited scalability. We use our framework to prove that (i) there is a theoretical imperative to use non-MF approaches, to avoid excessive bias in the process noise hyperparameter estimate, and (ii) we can parameterise only the posterior dynamics without any less of performance. Our approach suggests further approximations, based on the existing rich literature on filtering and smoothing for nonlinear systems, and unifies approaches for discrete and continuous time models.

Contrasting Discrete and Continuous Methods for Bayesian System Identification

Talay M Cheema, 2022. (In Workshop on Continuous Time Machine Learning at the 39th International Conference on Machine Learning).

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In recent years, there has been considerable interest in embedding continuous time methods in machine learning algorithms. In system identification, the task is to learn a dynamical model from incomplete observation data, and when prior knowledge is in continuous time – for example, mechanistic differential equation models – it seems natural to use continuous time models for learning. Yet when learning flexible, nonlinear, probabilistic dynamics models, most previous work has focused on discrete time models to avoid computational, numerical, and mathematical difficulties. In this work we show, with the aid of small-scale examples, that this mismatch between model and data generating process can be consequential under certain circumstances, and we discuss possible modifications to discrete time models which may better suit them to handling data generated by continuous time processes.

Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction

Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato, 2022. (arXiv).

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We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs) by interpolating between meta-learning and conventional deep kernel learning. Our approach employs a bilevel optimization objective where we meta-learn generally useful feature representations across tasks, in the sense that task-specific GP models estimated on top of such features achieve the lowest possible predictive loss on average. We solve the resulting nested optimization problem using the implicit function theorem (IFT). We show that our ADKF-IFT framework contains previously proposed Deep Kernel Learning (DKL) and Deep Kernel Transfer (DKT) as special cases. Although ADKF-IFT is a completely general method, we argue that it is especially well-suited for drug discovery problems and demonstrate that it significantly outperforms previous state-of-the-art methods on a variety of real-world few-shot molecular property prediction tasks and out-of-domain molecular property prediction and optimization tasks.

The unreasonable effectiveness of structured random orthogonal embeddings

Krzysztof Choromanski, Mark Rowland, Adrian Weller, December 2017. (In Advances in Neural Information Processing Systems 31). Long Beach, California.

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We examine a class of embeddings based on structured random matrices with orthogonal rows which can be applied in many machine learning applications including dimensionality reduction and kernel approximation. For both the Johnson-Lindenstrauss transform and the angular kernel, we show that we can select matrices yielding guaranteed improved performance in accuracy and/or speed compared to earlier methods. We introduce matrices with complex entries which give significant further accuracy improvement. We provide geometric and Markov chain-based perspectives to help understand the benefits, and empirical results which suggest that the approach is helpful in a wider range of applications.

Gaussian Processes for Ordinal Regression

Wei Chu, Zoubin Ghahramani, 2005. (Journal of Machine Learning Research).

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We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation propagation algorithm respectively, are derived for hyperparameter learning and model selection. We compare these two Gaussian process approaches with a previous ordinal regression method based on support vector machines on some benchmark and real-world data sets, including applications of ordinal regression to collaborative filtering and gene expression analysis. Experimental results on these data sets verify the usefulness of our approach.

Preference learning with Gaussian processes

Wei Chu, Zoubin Ghahramani, 2005. (In ICML). Edited by Luc De Raedt, Stefan Wrobel. acm. ACM International Conference Proceeding Series. ISBN: 1-59593-180-5.

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In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relations in the Bayesian framework. The generalized formulation is also applicable to tackle many multiclass problems. The overall approach has the advantages of Bayesian methods for model selection and probabilistic prediction. Experimental results compared against the constraint classification approach on several benchmark datasets verify the usefulness of this algorithm.

Relational learning with Gaussian processes

W. Chu, V. Sindhwani, Z. Ghahramani, S. Keerthi, September 2007. (In Advances in Neural Information Processing Systems 19). Edited by B. Schölkopf, J. Platt, T. Hofmann. Cambridge, MA, USA. The MIT Press. Bradford Books. Note: Online contents gives pages 314–321, and 289–296 on pdf of contents.

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Correlation between instances is often modelled via a kernel function using input attributes of the instances. Relational knowledge can further reveal additional pairwise correlations between variables of interest. In this paper, we develop a class of models which incorporates both reciprocal relational information and input attributes using Gaussian process techniques. This approach provides a novel non-parametric Bayesian framework with a data-dependent prior for supervised learning tasks. We also apply this framework to semi-supervised learning. Experimental results on several real world data sets verify the usefulness of this algorithm.

Stimulus onset quashes neural variability: a widespread cortical phenomenon

M. M. Churchland, B. M. Yu, J. P. Cunningham, L. P. Sugrue, M. R. Cohen, G. S. Corrado, W. T. Newsome, A. M. Clark, P. Hosseini, B. B. Scott, D. C. Bradley, M. A. Smith, A. Kohn, J. A. Movshon, K. M. Armstrong, T. Moore, S. W. Chang, L. H. Snyder, S. G. Lisberger, N. J. Priebe, I. M. Finn, D. Ferster, S. I. Ryu, G. Santhanam, M. Sahani, K. V. Shenoy., 2010. (Nature Neuro).

Abstract▼ URL

Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the mean response, but few have examined the effect on response variability. We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests a rather general property of cortex, that its state is stabilized by an input.

Derivation of Expectation Propagation for "Fast Gaussian process methods for point process intensity estimation"

J. P. Cunningham, 2008. Stanford University,

Abstract▼ URL

We derive the Expectation Propagation algorithm updates for approximating the posterior distribution on intensity in a conditionally inhomogeneous gamma interval process with a Gaussian Process prior (GP IGIP), a model which appeared in Cunningham, Shenoy, Sahani (2008) ICML.

Gaussian Processes for time-marked time-series data

John P. Cunningham, Zoubin Ghahramani, Carl Edward Rasmussen, 2012. (In 15th International Conference on Artificial Intelligence and Statistics).

Abstract▼ URL

In many settings, data is collected as multiple time series, where each recorded time series is an observation of some underlying dynamical process of interest. These observations are often time-marked with known event times, and one desires to do a range of standard analyses. When there is only one time marker, one simply aligns the observations temporally on that marker. When multiple time-markers are present and are at different times on different time series observations, these analyses are more difficult. We describe a Gaussian Process model for analyzing multiple time series with multiple time markings, and we test it on a variety of data.

Fast Gaussian process methods for point process intensity estimation

J. P. Cunningham, K. V. Shenoy, M. Sahani, June 2008. (In 25th International Conference on Machine Learning). Helsinki, Finland.

Abstract▼ URL

Point processes are difficult to analyze because they provide only a sparse and noisy observation of the intensity function driving the process. Gaussian Processes offer an attractive framework within which to infer underlying intensity functions. The result of this inference is a continuous function defined across time that is typically more amenable to analytical efforts. However, a naive implementation will become computationally infeasible in any problem of reasonable size, both in memory and run time requirements. We demonstrate problem specific methods for a class of renewal processes that eliminate the memory burden and reduce the solve time by orders of magnitude.

Inferring neural firing rates from spike trains using Gaussian processes

J. P. Cunningham, B. M. Yu, K. V. Shenoy, M. Sahani, December 2008. (In Advances in Neural Information Processing Systems 20). Vancouver, BC.

Abstract▼ URL

Neural spike trains present challenges to analytical efforts due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smoothed, denoised estimate of the spike train’s underlying firing rate. Current techniques to find time-varying firing rates require ad hoc choices of parameters, offer no confidence intervals on their estimates, and can obscure potentially important single trial variability. We present a new method, based on a Gaussian Process prior, for inferring probabilistically optimal estimates of firing rate functions underlying single or multiple neural spike trains. We test the performance of the method on simulated data and experimentally gathered neural spike trains, and we demonstrate improvements over conventional estimators.

Comment: Spotlight Presentation

Effective implementation of Gaussian process regression for machine learning

Alex Davies, 2015. University of Cambridge, Department of Engineering, Cambridge, UK.

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This thesis presents frameworks for the effective implementation of Gaussian process regression for machine learning. It addresses this in three parts: effective iterative methods for calculating the predictive distribution and derivatives of a Gaussian process with fixed hyper-parameters, defining three broad classes of kernels of controllable complexity that allow for an order of magnitude scaling in the previous framework and an investigation into alternative objective functions and improved derivatives for the optimization of model hyper-parameters.

The Random Forest Kernel and other kernels for big data from random partitions

Alex Davies, Zoubin Ghahramani, 2014. (arXiv).

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We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing O(N) inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.

Efficient Reinforcement Learning using Gaussian Processes

Marc Peter Deisenroth, 2010. Karlsruhe Institute of Technology, Karlsruhe, Germany.

Abstract▼ URL

In many research areas, including control and medical applications, we face decision-making problems where data are limited and/or the underlying generative process is complicated and partially unknown. In these scenarios, we can profit from algorithms that learn from data and aid decision making. Reinforcement learning (RL) is a general computational approach to experience-based goal-directed learning for sequential decision making under uncertainty. However, RL often lacks efficiency in terms of the number of required trials when no task-specific knowledge is available. This lack of efficiency makes RL often inapplicable to (optimal) control problems. Thus, a central issue in RL is to speed up learning by extracting more information from available experience. The contributions of this dissertation are threefold: 1. We propose PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO is based on well-established ideas from statistics and machine learning. PILCO’s key ingredient is a probabilistic dynamics model learned from data, which is implemented by a Gaussian process (GP). The GP carefully quantifies knowledge by a probability distribution over plausible dynamics models. By averaging over all these models during long-term planning and decision making, PILCO takes uncertainties into account in a principled way and, therefore, reduces model bias, a central problem in model-based RL. 2. Due to its generality and efficiency, PILCO can be considered a conceptual and practical approach to jointly learning models and controllers when expert knowledge is difficult to obtain or simply not available. For this scenario, we investigate PILCO’s properties its applicability to challenging real and simulated nonlinear control problems. For example, we consider the tasks of learning to swing up a double pendulum attached to a cart or to balance a unicycle with five degrees of freedom. Across all tasks we report unprecedented automation and an unprecedented learning efficiency for solving these tasks. 3. As a step toward pilco’s extension to partially observable Markov decision processes, we propose a principled algorithm for robust filtering and smoothing in GP dynamic systems. Unlike commonly used Gaussian filters for nonlinear systems, it does neither rely on function linearization nor on finite-sample representations of densities. Our algorithm profits from exact moment matching for predictions while keeping all computations analytically tractable. We present experimental evidence that demonstrates the robustness and the advantages of our method over unscented Kalman filters, the cubature Kalman filter, and the extended Kalman filter.

Gaussian Processes for Data-Efficient Learning in Robotics and Control

Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen, 2015. (IEEE Transactions on Pattern Analysis and Machine Intelligence). DOI: 10.1109/TPAMI.2013.218.

Abstract▼

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this article, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

Analytic Moment-based Gaussian Process Filtering

Marc Peter Deisenroth, Marco F. Huber, Uwe D. Hanebeck, June 2009. (In 26th International Conference on Machine Learning). Edited by Léon Bottou, Michael Littman. Montréal, QC, Canada. Omnipress.

Abstract▼ URL

We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes. Exact expressions for the expected value and the covariance matrix are provided for both the prediction step and the filter step, where an additional Gaussian assumption is exploited in the latter case. Our filter does not require further approximations. In particular, it avoids finite-sample approximations. We compare the filter to a variety of Gaussian filters, that is, the EKF, the UKF, and the recent GP-UKF proposed by Ko et al. (2007).

Comment: With corrections. code.

Approximate Dynamic Programming with Gaussian Processes

Marc Peter Deisenroth, Jan Peters, Carl Edward Rasmussen, June 2008. (In 2008 American Control Conference (ACC 2008)). Seattle, WA, USA.

Abstract▼ URL

In general, it is difficult to determine an optimal closed-loop policy in nonlinear control problems with continuous-valued state and control domains. Hence, approximations are often inevitable. The standard method of discretizing states and controls suffers from the curse of dimensionality and strongly depends on the chosen temporal sampling rate. The paper introduces Gaussian Process Dynamic Programming (GPDP). In GPDP, value functions in the Bellman recursion of the dynamic programming algorithm are modeled using Gaussian processes. GPDP returns an optimal state-feedback for a finite set of states. Based on these outcomes, we learn a possibly discontinuous closed-loop policy on the entire state space by switching between two independently trained Gaussian processes.

Comment: code.

Model-Based Reinforcement Learning with Continuous States and Actions

Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters, April 2008. (In Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN 2008)). Bruges, Belgium.

Abstract▼ URL

Finding an optimal policy in a reinforcement learning (RL) framework with continuous state and action spaces is challenging. Approximate solutions are often inevitable. GPDP is an approximate dynamic programming algorithm based on Gaussian process (GP) models for the value functions. In this paper, we extend GPDP to the case of unknown transition dynamics. After building a GP model for the transition dynamics, we apply GPDP to this model and determine a continuous-valued policy in the entire state space. We apply the resulting controller to the underpowered pendulum swing up. Moreover, we compare our results on this RL task to a nearly optimal discrete DP solution in a fully known environment.

Comment: code. slides

Gaussian process dynamic programming

Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters, March 2009. (Neurocomputing). Elsevier B. V.. DOI: 10.1016/j.neucom.2008.12.019.

Abstract▼ URL

Reinforcement learning (RL) and optimal control of systems with continuous states and actions require approximation techniques in most interesting cases. In this article, we introduce Gaussian process dynamic programming (GPDP), an approximate value function-based RL algorithm. We consider both a classic optimal control problem, where problem-specific prior knowledge is available, and a classic RL problem, where only very general priors can be used. For the classic optimal control problem, GPDP models the unknown value functions with Gaussian processes and generalizes dynamic programming to continuous-valued states and actions. For the RL problem, GPDP starts from a given initial state and explores the state space using Bayesian active learning. To design a fast learner, available data have to be used efficiently. Hence, we propose to learn probabilistic models of the a priori unknown transition dynamics and the value functions on the fly. In both cases, we successfully apply the resulting continuous-valued controllers to the under-actuated pendulum swing up and analyze the performances of the suggested algorithms. It turns out that GPDP uses data very efficiently and can be applied to problems, where classic dynamic programming would be cumbersome.

Comment: code.

Robust Filtering and Smoothing with Gaussian Processes

Marc Peter Deisenroth, Ryan D. Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen, 2012. (IEEE Transactions on Automatic Control). DOI: 10.1109/TAC.2011.2179426.

Abstract▼ URL

We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by nonparametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of “system identification” is more robust than finding point estimates of a parametric function representation. Our principled filtering/smoothing approach for GP dynamic systems is based on analytic moment matching in the context of the forward-backward algorithm. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail.

Sparse Gaussian Processes with Spherical Harmonic Features

Vincent Dutordoir, Nicolas Durrande, James Hensman, June 2020. (In 37th International Conference on Machine Learning). Online.

Abstract▼ URL

We introduce a new class of inter-domain variational Gaussian processes (GP) where data is mapped onto the unit hypersphere in order to use spherical harmonic representations. Our inference scheme is comparable to variational Fourier features, but it does not suffer from the curse of dimensionality, and leads to diagonal covariance matrices between inducing variables. This enables a speed-up in inference, because it bypasses the need to invert large covariance matrices. Our experiments show that our model is able to fit a regression model for a dataset with 6 million entries two orders of magnitude faster compared to standard sparse GPs, while retaining state of the art accuracy. We also demonstrate competitive performance on classification with non-conjugate likelihoods.

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande, Dec 2021. (In Advances in Neural Information Processing Systems 34). Online.

Abstract▼ URL

Neural networks and Gaussian processes are complementary in their strengths and weaknesses. Having a better understanding of their relationship comes with the promise to make each method benefit from the strengths of the other. In this work, we establish an equivalence between the forward passes of neural networks and (deep) sparse Gaussian process models. The theory we develop is based on interpreting activation functions as interdomain inducing features through a rigorous analysis of the interplay between activation functions and kernels. This results in models that can either be seen as neural networks with improved uncertainty prediction or deep Gaussian processes with increased prediction accuracy. These claims are supported by experimental results on regression and classification datasets.

Gaussian Process Conditional Density Estimation

Vincent Dutordoir, Hugh Salimbeni, Marc Deisenroth, James Hensman, Dec 2018. (In Advances in Neural Information Processing Systems 32). Montréal, Canada.

Abstract▼ URL

Conditional Density Estimation (CDE) models deal with estimating conditional distributions. The conditions imposed on the distribution are the inputs of the model. CDE is a challenging task as there is a fundamental trade-off between model complexity, representational capacity and overfitting. In this work, we propose to extend the model’s input with latent variables and use Gaussian processes (GP) to map this augmented input onto samples from the conditional distribution. Our Bayesian approach allows for the modeling of small datasets, but we also provide the machinery for it to be applied to big data using stochastic variational inference. Our approach can be used to model densities even in sparse data regions, and allows for sharing learned structure between conditions. We illustrate the effectiveness and wide-reaching applicability of our model on a variety of real- world problems, such as spatio-temporal density estimation of taxi drop-offs, non-Gaussian noise modeling, and few-shot learning on omniglot images.

Neural Diffusion Processes

Vincent Dutordoir, Alan Saul, Zoubin Ghahramani, Fergus Simpson, Apr 2022. (In arXiv). Online.

Abstract▼ URL

Gaussian processes provide an elegant framework for specifying prior and posterior distributions over functions. They are, however, also computationally expensive, and limited by the expressivity of their covariance function. We propose Neural Diffusion Processes (NDPs), a novel approach based upon diffusion models, that learn to sample from distributions over functions. Using a novel attention block, we can incorporate properties of stochastic processes, such as exchangeability, directly into the NDP’s architecture. We empirically show that NDPs are able to capture functional distributions that are close to the true Bayesian posterior of a Gaussian process. This enables a variety of downstream tasks, including hyperparameter marginalisation and Bayesian optimisation.

Additive Gaussian Processes

David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen, 2011. (In Advances in Neural Information Processing Systems 24). Granada, Spain.

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We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-exponential kernels. Hyperparameter learning in this model can be seen as Bayesian Hierarchical Kernel Learning (HKL). We introduce an expressive but tractable parameterization of the kernel function, which allows efficient evaluation of all input interaction terms, whose number is exponential in the input dimension. The additional structure discoverable by this model results in increased interpretability, as well as state-of-the-art predictive power in regression tasks.

Avoiding pathologies in very deep networks

David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani, April 2014. (In 17th International Conference on Artificial Intelligence and Statistics). Reykjavik, Iceland.

Abstract▼ URL

Choosing appropriate architectures and regularization strategies for deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions. Specifically, we study the deep Gaussian process, a type of infinitely-wide, deep neural network. We show that in standard architectures, the representational capacity of the network tends to capture fewer degrees of freedom as the number of layers increases, retaining only a single degree of freedom in the limit. We propose an alternate network architecture which does not suffer from this pathology. We also examine deep covariance functions, obtained by composing infinitely many feature transforms. Lastly, we characterize the class of models obtained by performing dropout on Gaussian processes.

Bayesian Time Series Learning with Gaussian Processes

Roger Frigola, 2015. University of Cambridge, Department of Engineering, Cambridge, UK.

Abstract▼ URL

The analysis of time series data is important in fields as disparate as the social sciences, biology, engineering or econometrics. In this dissertation, we present a number of algorithms designed to learn Bayesian nonparametric models of time series. The goal of these kinds of models is twofold. First, they aim at making predictions which quantify the uncertainty due to limitations in the quantity and the quality of the data. Second, they are flexible enough to model highly complex data whilst preventing overfitting when the data does not warrant complex models. We begin with a unifying literature review on time series models based on Gaussian processes. Then, we centre our attention on the Gaussian Process State-Space Model (GP-SSM): a Bayesian nonparametric generalisation of discrete-time nonlinear state-space models. We present a novel formulation of the GP-SSM that offers new insights into its properties. We then proceed to exploit those insights by developing new learning algorithms for the GP-SSM based on particle Markov chain Monte Carlo and variational inference. Finally, we present a filtered nonlinear auto-regressive model with a simple, robust and fast learning algorithm that makes it well suited to its application by non-experts on large datasets. Its main advantage is that it avoids the computationally expensive (and potentially difficult to tune) smoothing step that is a key part of learning nonlinear state-space models.

Variational Gaussian Process State-Space Models

Roger Frigola, Yutian Chen, Carl Edward Rasmussen, 2014. (In Advances in Neural Information Processing Systems 27). Edited by Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger.

Abstract▼ URL

State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian learning of nonlinear state-space models based on sparse Gaussian processes. The result of learning is a tractable posterior over nonlinear dynamical systems. In comparison to conventional parametric models, we offer the possibility to straightforwardly trade off model capacity and computational cost whilst avoiding overfitting. Our main algorithm uses a hybrid inference approach combining variational Bayes and sequential Monte Carlo. We also present stochastic variational inference and online learning approaches for fast learning with long time series.

Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC

Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl Edward Rasmussen, 2013. (In Advances in Neural Information Processing Systems 26). Edited by L. Bottou, C.J.C. Burges, Z. Ghahramani, M. Welling, K.Q. Weinberger. Curran Associates, Inc..

Abstract▼ URL

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning in nonlinear nonparametric state-space models. We place a Gaussian process prior over the transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. However, to enable efficient inference, we marginalize over the dynamics of the model and instead infer directly the joint smoothing distribution through the use of specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. We make use of sparse Gaussian process models to greatly reduce the computational complexity of the approach.

Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM

Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl Edward Rasmussen, 2014. (In Proceedings of the 19th World Congress of the International Federation of Automatic Control (IFAC)).

Abstract▼ URL

Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dynamics of the system and additional (hyper-)parameters governing the properties of this nonparametric representation. The Bayesian formalism enables systematic reasoning about the uncertainty in the system dynamics. We present an approach to maximum likelihood identification of the parameters in GP-SSMs, while retaining the full nonparametric description of the dynamics. The method is based on a stochastic approximation version of the EM algorithm that employs recent developments in particle Markov chain Monte Carlo for efficient identification.

Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes

Roger Frigola, Carl Edward Rasmussen, 2013. (In Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on).

Abstract▼ URL

We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is tackled using sparse Gaussian processes (GP). We integrate data pre-processing with system identification into a fully automated procedure that goes from raw data to an identified model. Both pre-processing parameters and GP hyper-parameters are tuned by maximizing the marginal likelihood of the probabilistic model. We obtain a Bayesian model of the system’s dynamics which is able to report its uncertainty in regions where the data is scarce. The automated approach, the modeling of uncertainty and its relatively low computational cost make of GP-FNARX a good candidate for applications in robotics and adaptive control.

Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data

Yarin Gal, Yutian Chen, Zoubin Ghahramani, 2015. (In Proceedings of the 32nd International Conference on Machine Learning (ICML-15)).

Abstract▼ URL

Multivariate categorical data occur in many applications of machine learning. One of the main difficulties with these vectors of categorical variables is sparsity. The number of possible observations grows exponentially with vector length, but dataset diversity might be poor in comparison. Recent models have gained significant improvement in supervised tasks with this data. These models embed observations in a continuous space to capture similarities between them. Building on these ideas we propose a Bayesian model for the unsupervised task of distribution estimation of multivariate categorical data. We model vectors of categorical variables as generated from a non-linear transformation of a continuous latent space. Non-linearity captures multi-modality in the distribution. The continuous representation addresses sparsity. Our model ties together many existing models, linking the linear categorical latent Gaussian model, the Gaussian process latent variable model, and Gaussian process classification. We derive inference for our model based on recent developments in sampling based variational inference. We show empirically that the model outperforms its linear and discrete counterparts in imputation tasks of sparse data.

Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs

Yarin Gal, Richard Turner, 2015. (In Proceedings of the 32nd International Conference on Machine Learning (ICML-15)).

Abstract▼ URL

Standard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to over-fit. We suggest the use of variational inference for the sparse spectrum approximation to avoid both issues. We model the covariance function with a finite Fourier series approximation and treat it as a random variable. The random covariance function has a posterior, on which a variational distribution is placed. The variational distribution transforms the random covariance function to fit the data. We study the properties of our approximate inference, compare it to alternative ones, and extend it to the distributed and stochastic domains. Our approximation captures complex functions better than standard approaches and avoids over-fitting.

Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models

Yarin Gal, Mark van der Wilk, Carl Rasmussen, 2014. (In Advances in Neural Information Processing Systems 27). Edited by Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, K.Q. Weinberger. Curran Associates, Inc..

Abstract▼ URL

Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have been applied to both regression and non-linear dimensionality reduction, and offer desirable properties such as uncertainty estimates, robustness to over-fitting, and principled ways for tuning hyper-parameters. However the scalability of these models to big datasets remains an active topic of research. We introduce a novel re-parametrisation of variational inference for sparse GP regression and latent variable models that allows for an efficient distributed algorithm. This is done by exploiting the decoupling of the data given the inducing points to re-formulate the evidence lower bound in a Map-Reduce setting. We show that the inference scales well with data and computational resources, while preserving a balanced distribution of the load among the nodes. We further demonstrate the utility in scaling Gaussian processes to big data. We show that GP performance improves with increasing amounts of data in regression (on flight data with 2 million records) and latent variable modelling (on MNIST). The results show that GPs perform better than many common models often used for big data.

Deep Convolutional Networks as shallow Gaussian Processes

Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison, 2019. (In International Conference on Learning Representations (ICLR)).

Abstract▼ URL

We show that the output of a (residual) convolutional neural network (CNN) with an appropriate prior over the weights and biases is a Gaussian process (GP) in the limit of infinitely many convolutional filters, extending similar results for dense networks. For a CNN, the equivalent kernel can be computed exactly and, unlike “deep kernels”, has very few parameters: only the hyperparameters of the original CNN. Further, we show that this kernel has two properties that allow it to be computed efficiently; the cost of evaluating the kernel for a pair of images is similar to a single forward pass through the original CNN with only one filter per layer. The kernel equivalent to a 32-layer ResNet obtains 0.84% classification error on MNIST, a new record for GPs with a comparable number of parameters.

Scaling Multidimensional Gaussian Processes using Projected Additive Approximations

E. Gilboa, Yunus Saatçi, John P. Cunningham, 2013. (In 30th International Conference on Machine Learning).

Abstract▼ URL

Exact Gaussian Process (GP) regression has O(N3) runtime for data size N, making it intractable for large N. Many algorithms for improving GP scaling approximate the covariance with lower rank matrices. Other work has exploited structure inherent in particular covariance functions, including GPs with implied Markov structure, and equispaced inputs (both enable O(N) runtime). However, these GP advances have not been extended to the multidimensional input setting, despite the preponderance of multidimensional applications. This paper introduces and tests novel extensions of structured GPs to multidimensional inputs. We present new methods for additive GPs, showing a novel connection between the classic backfitting method and the Bayesian framework. To achieve optimal accuracy-complexity tradeoff, we extend this model with a novel variant of projection pursuit regression. Our primary result – projection pursuit Gaussian Process Regression – shows orders of magnitude speedup while preserving high accuracy. The natural second and third steps include non-Gaussian observations and higher dimensional equispaced grid methods. We introduce novel techniques to address both of these necessary directions. We thoroughly illustrate the power of these three advances on several datasets, achieving close performance to the naive Full GP at orders of magnitude less cost.

Scaling Multidimensional Inference for Structured Gaussian Processes

E. Gilboa, Yunus Saatçi, John P. Cunningham, 2015. (IEEE Transactions on Pattern Analysis and Machine Intelligence). DOI: 10.1109/TPAMI.2013.192.

Abstract▼

Exact Gaussian process (GP) regression has O(N3 runtime for data size N, making it intractable for large N. Many algorithms for improving GP scaling approximate the covariance with lower rank matrices. Other work has exploited structure inherent in particular covariance functions, including GPs with implied Markov structure, and inputs on a lattice (both enable O(N) or O(N log N) runtime). However, these GP advances have not been well extended to the multidimensional input setting, despite the preponderance of multidimensional applications. This paper introduces and tests three novel extensions of structured GPs to multidimensional inputs, for models with additive and multiplicative kernels. First we present a new method for inference in additive GPs, showing a novel connection between the classic backfitting method and the Bayesian framework. We extend this model using two advances: a variant of projection pursuit regression, and a Laplace approximation for non-Gaussian observations. Lastly, for multiplicative kernel structure, we present a novel method for GPs with inputs on a multidimensional grid. We illustrate the power of these three advances on several data sets, achieving performance equal to or very close to the naive GP at orders of magnitude less cost.

Comment: arXiv

Gaussian Process priors with uncertain inputs — application to multiple-step ahead time series forecasting

Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero-Candela, Roderick Murray-Smith, December 2003. (In Advances in Neural Information Processing Systems 15). Edited by S. Becker, S. Thrun, K. Obermayer. Cambridge, MA, USA. The MIT Press.

Abstract▼ URL

We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form yt = f(yt-1,…,yt-L), the prediction of y at time t + k is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction.

Modelling and Control of Nonlinear Systems using Gaussian Processes with Partial Model Information

Joseph Hall, Carl Edward Rasmussen, Jan Maciejowski, 2012. (In 51st IEEE Conference on Decision and Control).

Abstract▼ URL

Gaussian processes are gaining increasing popularity among the control community, in particular for the modelling of discrete time state space systems. However, it has not been clear how to incorporate model information, in the form of known state relationships, when using a Gaussian process as a predictive model. An obvious example of known prior information is position and velocity related states. Incorporation of such information would be beneficial both computationally and for faster dynamics learning. This paper introduces a method of achieving this, yielding faster dynamics learning and a reduction in computational effort from O(Dn2) to O((D-F)n2) in the prediction stage for a system with D states, F known state relationships and n observations. The effectiveness of the method is demonstrated through its inclusion in the PILCO learning algorithm with application to the swing-up and balance of a torque-limited pendulum and the balancing of a robotic unicycle in simulation.

MCMC for Variationally Sparse Gaussian Processes

James Hensman, Alexander G D G Matthews, Maurizio Filippone, Zoubin Ghahramani, December 2015. (In Advances in Neural Information Processing Systems 28). Montreal, Canada.

Abstract▼ URL

Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable research effort has been made into attacking three issues with GP models: how to compute efficiently when the number of data is large; how to approximate the posterior when the likelihood is not Gaussian and how to estimate covariance function parameter posteriors. This paper simultaneously addresses these, using a variational approximation to the posterior which is sparse in support of the function but otherwise free-form. The result is a Hybrid Monte-Carlo sampling scheme which allows for a non-Gaussian approximation over the function values and covariance parameters simultaneously, with efficient computations based on inducing-point sparse GPs. Code to replicate each experiment in this paper will be available shortly.

Scalable Variational Gaussian Process Classification

James Hensman, Alexander G D G Matthews, Zoubin Ghahramani, May 2015. (In 18th International Conference on Artificial Intelligence and Statistics). San Diego, California, USA.

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Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, out-performing the state of the art on benchmark datasets. Importantly, the variational formulation an be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments.

Gaussian Process Conditional Copulas with Applications to Financial Time Series

José Miguel Hernández-Lobato, James Robert Lloyds, Daniel Hernández-Lobato, December 2013. (In Advances in Neural Information Processing Systems 27). Lake Tahoe, California, USA.

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The estimation of dependencies between multiple variables is a central problem in the analysis of financial time series. A common approach is to express these dependencies in terms of a copula function. Typically the copula function is assumed to be constant but this may be inaccurate when there are covariates that could have a large influence on the dependence structure of the data. To account for this, a Bayesian framework for the estimation of conditional copulas is proposed. In this framework the parameters of a copula are non-linearly related to some arbitrary conditioning variables. We evaluate the ability of our method to predict time-varying dependencies on several equities and currencies and observe consistent performance gains compared to static copula models and other time-varying copula methods.

Collaborative Gaussian Processes for Preference Learning

Neil Houlsby, Jose Miguel Hernández-Lobato, Ferenc Huszár, Zoubin Ghahramani, 2012. (In Advances in Neural Information Processing Systems 26). Curran Associates, Inc..

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We present a new model based on Gaussian processes (GPs) for learning pairwise preferences expressed by multiple users. Inference is simplified by using a preference kernel for GPs which allows us to combine supervised GP learning of user preferences with unsupervised dimensionality reduction for multi-user systems. The model not only exploits collaborative information from the shared structure in user behavior, but may also incorporate user features if they are available. Approximate inference is implemented using a combination of expectation propagation and variational Bayes. Finally, we present an efficient active learning strategy for querying preferences. The proposed technique performs favorably on real-world data against state-of-the-art multi-user preference learning algorithms.

Optimally-Weighted Herding is Bayesian Quadrature

Ferenc Huszár, David Duvenaud, July 2012. (In 28th Conference on Uncertainty in Artificial Intelligence). Catalina Island, California.

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Herding and kernel herding are deterministic methods of choosing samples which summarise a probability distribution. A related task is choosing samples for estimating integrals using Bayesian quadrature. We show that the criterion minimised when selecting samples in kernel herding is equivalent to the posterior variance in Bayesian quadrature. We then show that sequential Bayesian quadrature can be viewed as a weighted version of kernel herding which achieves performance superior to any other weighted herding method. We demonstrate empirically a rate of convergence faster than O(1/N). Our results also imply an upper bound on the empirical error of the Bayesian quadrature estimate.

Variational Inference in Dynamical Systems

Alessandro Davide Ialongo, 2022. University of Cambridge, Department of Engineering, Cambridge, UK. DOI: https://doi.org/10.17863/CAM.91368.

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Dynamical systems are a powerful formalism to analyse the world around us. Many datasets are sequential in nature, and can be described by a discrete time evolution law. We are interested in approaching the analysis of such datasets from a probabilistic perspective. We would like to maintain justified beliefs about quantities which, though useful in explaining the behaviour of a system, may not be observable, as well as about the system’s evolution itself, especially in regimes we have not yet observed in our data. The framework of statistical inference gives us the tools to do so, yet, for many systems of interest, performing inference exactly is not computationally or analytically tractable. The contribution of this thesis, then, is twofold: first, we uncover two sources of bias in existing variational inference methods applied to dynamical systems in general, and state space models whose transition function is drawn from a Gaussian process (GPSSM) in particular. We show bias can derive from assuming posteriors in non-linear systems to be jointly Gaussian, and from assuming that we can sever the dependence between latent states and transition function in state space model posteriors. Second, we propose methods to address these issues, undoing the resulting biases. We do this without compromising on computational efficiency or on the ability to scale to larger datasets and higher dimensions, compared to the methods we rectify. One method, the Markov Autoregressive Flow (Markov AF) addresses the Gaussian assumption, by providing a more flexible class of posteriors, based on normalizing flows, which can be easily evaluated, sampled, and optimised. The other method, Variationally Coupled Dynamics and Trajectories (VCDT), tackles the factorisation assumption, leveraging sparse Gaussian processes and their variational representation to reintroduce dependence between latent states and the transition function at no extra computational cost. Since the objective of inference is to maintain calibrated beliefs, if we employed approximations which are significantly biased in non-linear, noisy systems, or when there is little data available, we would have failed in our objective, as those are precisely the regimes in which uncertainty quantification is all the more important. Hence we think it is essential, if we wish to act optimally on such beliefs, to uncover, and, if possible, to correct, all sources of systematic bias in our inference methods.

Non-Factorised Variational Inference in Dynamical Systems

Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen, December 2018. (In First Symposium on Advances in Approximate Bayesian Inference). Montreal.

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We focus on variational inference in dynamical systems where the discrete time transition function (or evolution rule) is modelled by a Gaussian process. The dominant approach so far has been to use a factorised posterior distribution, decoupling the transition function from the system states. This is not exact in general and can lead to an overconfident posterior over the transition function as well as an overestimation of the intrinsic stochasticity of the system (process noise). We propose a new method that addresses these issues and incurs no additional computational costs.

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen, June 2019. (In 36th International Conference on Machine Learning). Long Beach.

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We identify a new variational inference scheme for dynamical systems whose transition function is modelled by a Gaussian process. Inference in this setting has either employed computationally intensive MCMC methods, or relied on factorisations of the variational posterior. As we demonstrate in our experiments, the factorisation between latent system states and transition function can lead to a miscalibrated posterior and to learning unnecessarily large noise terms. We eliminate this factorisation by explicitly modelling the dependence between state trajectories and the Gaussian process posterior. Samples of the latent states can then be tractably generated by conditioning on this representation. The method we obtain (VCDT: variationally coupled dynamics and trajectories) gives better predictive performance and more calibrated estimates of the transition function, yet maintains the same time and space complexities as mean-field methods. Code is available at: https://github.com/ialong/GPt.

Closed-form Inference and Prediction in Gaussian Process State-Space Models

Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen, December 2017. (In NIPS Time Series Workshop 2017). Long Beach.

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We examine an analytic variational inference scheme for the Gaussian Process State Space Model (GPSSM) - a probabilistic model for system identification and time-series modelling. Our approach performs variational inference over both the system states and the transition function. We exploit Markov structure in the true posterior, as well as an inducing point approximation to achieve linear time complexity in the length of the time series. Contrary to previous approaches, no Monte Carlo sampling is required: inference is cast as a deterministic optimisation problem. In a number of experiments, we demonstrate the ability to model non-linear dynamics in the presence of both process and observation noise as well as to impute missing information (e.g. velocities from raw positions through time), to de-noise, and to estimate the underlying dimensionality of the system. Finally, we also introduce a closed-form method for multi-step prediction, and a novel criterion for assessing the quality of our approximate posterior.

Warped Mixtures for Nonparametric Cluster Shapes

Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani, July 2013. (In 29th Conference on Uncertainty in Artificial Intelligence). Bellevue, Washington.

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A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. The number of manifolds, as well as the shape and dimension of each manifold is automatically inferred. We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. We show that our model is effective for density estimation, performs better than infinite Gaussian mixture models at recovering the true number of clusters, and produces interpretable summaries of high-dimensional datasets.

Bandit optimisation of functions in the Matérn kernel RKHS

David Janz, David Burt, Javier Gonzalez, 2020. (In 23rd International Conference on Artificial Intelligence and Statistics).

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We consider the problem of optimising functions in the reproducing kernel Hilbert space (RKHS) of a Matérn kernel with smoothness parameter u over the domain [0,1]^d under noisy bandit feedback. Our contribution, the π-GP-UCB algorithm, is the first practical approach with guaranteed sublinear regret for all u gt;1 and d ≥ 1. Empirical validation suggests better performance and drastically improved computational scalablity compared with its predecessor, Improved GP-UCB.

Scalable Gaussian Process Variational Autoencoders

Metod Jazbec, Matt Ashman, Vincent Fortuin, Michael Pearce, Stephan Mandt, Gunnar Rätsch, 13–15 Apr 2021. (In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics). Edited by Arindam Banerjee, Kenji Fukumizu. Proceedings of Machine Learning Research. Proceedings of Machine Learning Research.

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Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amortized Gaussian process inference through GP-VAEs has led to significant improvements in this regard, but is still inhibited by the intrinsic complexity of exact GP inference. We improve the scalability of these methods through principled sparse inference approaches. We propose a new scalable GP-VAE model that outperforms existing approaches in terms of runtime and memory footprint, is easy to implement, and allows for joint end-to-end optimization of all components.

Bayesian Gaussian Process Classification with the EM-EP Algorithm

Hyun-Chul Kim, Zoubin Ghahramani, 2006. (IEEE Trans. Pattern Anal. Mach. Intell.).

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Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically related to the value of some latent function at that location. Starting from a Gaussian process prior over this latent function, data are used to infer both the posterior over the latent function and the values of hyperparameters to determine various aspects of the function. Recently, the expectation propagation (EP) approach has been proposed to infer the posterior over the latent function. Based on this work, we present an approximate EM algorithm, the EM-EP algorithm, to learn both the latent function and the hyperparameters. This algorithm is found to converge in practice and provides an efficient Bayesian framework for learning hyperparameters of the kernel. A multiclass extension of the EM-EP algorithm for GPCs is also derived. In the experimental results, the EM-EP algorithms are as good or better than other methods for GPCs or Support Vector Machines (SVMs) with cross-validation.

Outlier robust Gaussian process classification

H. Kim, Zoubin Ghahramani, December 2008. (In Structural, Syntactic and Statistical Pattern Recognition). Edited by L. Niels da Vitoria. (Lecture Notes in Computer Science (LNCS)). Berlin, Germany. Springer Berlin / Heidelberg. Lecture Notes in Computer Science (LNCS).

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Gaussian process classifiers (GPCs) are a fully statistical model for kernel classification. We present a form of GPC which is robust to labeling errors in the data set. This model allows label noise not only near the class boundaries, but also far from the class boundaries which can result from mistakes in labelling or gross errors in measuring the input features. We derive an outlier robust algorithm for training this model which alternates iterations based on the EP approximation and hyperparameter updates until convergence. We show the usefulness of the proposed algorithm with model selection method through simulation results.

Outlier Robust Gaussian Process Classification

Hyun-Chul Kim, Zoubin Ghahramani, 2008. (In SSPR/SPR). Edited by Niels da Vitoria Lobo, Takis Kasparis, Fabio Roli, James Tin-Yau Kwok, Michael Georgiopoulos, Georgios C. Anagnostopoulos, Marco Loog. Springer. Lecture Notes in Computer Science. ISBN: 978-3-540-89688-3.

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Gaussian process classifiers (GPCs) are a fully statistical model for kernel classification. We present a form of GPC which is robust to labeling errors in the data set. This model allows label noise not only near the class boundaries, but also far from the class boundaries which can result from mistakes in labelling or gross errors in measuring the input features. We derive an outlier robust algorithm for training this model which alternates iterations based on the EP approximation and hyperparameter updates until convergence. We show the usefulness of the proposed algorithm with model selection method through simulation results.

Appearance-based gender classification with Gaussian processes

Hyun-Chul Kim, Daijin Kim, Zoubin Ghahramani, Sung Yang Bang, 2006. (Pattern Recognition Letters).

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This paper concerns the gender classification task of discriminating between images of faces of men and women from face images. In appearance-based approaches, the initial images are preprocessed (e.g. normalized) and input into classifiers. Recently, support vector machines (SVMs) which are popular kernel classifiers have been applied to gender classification and have shown excellent performance. SVMs have difficulty in determining the hyperparameters in kernels (using cross-validation). We propose to use Gaussian process classifiers (GPCs) which are Bayesian kernel classifiers. The main advantage of GPCs over SVMs is that they determine the hyperparameters of the kernel based on Bayesian model selection criterion. The experimental results show that our methods outperformed SVMs with cross-validation in most of data sets. Moreover, the kernel hyperparameters found by GPCs using Bayesian methods can be used to improve SVM performance.

A case based comparison of identification with neural network and Gaussian process models

Juš Kocijan, Blaž Banko, Bojan Likar, Agathe Girard, Roderick Murray-Smith, Carl Edward Rasmussen, 2003. (In IFAC Internaltional Conference on Intelligent Control Systems and Signal Processing).

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In this paper an alternative approach to black-box identification of non-linear dynamic systems is compared with the more established approach of using artificial neural networks. The Gaussian process prior approach is a representative of non-parametric modelling approaches. It was compared on a pH process modelling case study. The purpose of modelling was to use the model for control design. The comparison revealed that even though Gaussian process models can be effectively used for modelling dynamic systems caution has to be axercised when signals are selected.

Gaussian process model based predictive control

Juš Kocijan, Roderick Murray-Smith, Carl Edward Rasmussen, Agathe Girard, 2004. (In American Control Conference). (Proceedings of the ACC 2004). Boston, MA.

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Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identi cation of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coef cients to be optimised. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark.

Predictive control with Gaussian process models

Juš Kocijan, Roderick Murray-Smith, Carl Edward Rasmussen, Bojan Likar, 2003. (In IEEE Region 8 Eurocon 2003: Computer as a Tool). Edited by B. Zajc, M. Tkal.

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This paper describes model-based predictive control based on Gaussian processes. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. It offers more insight in variance of obtained model response, as well as fewer parameters to determine than other models. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. This property is used in predictive control, where optimisation of control signal takes the variance information into account. The predictive control principle is demonstrated on a simulated example of nonlinear system.

Scalable Magnetic Field SLAM in 3D Using Gaussian Process Maps

Manon Kok, Arno Solin, July 2018. (In Proceedings of the 21th International Conference on Information Fusion (accepted for publication)). Cambridge, UK.

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We present a method for scalable and fully 3D magnetic field simultaneous localisation and mapping (SLAM) using local anomalies in the magnetic field as a source of position information. These anomalies are due to the presence of ferromagnetic material in the structure of buildings and in objects such as furniture. We represent the magnetic field map using a Gaussian process model and take well-known physical properties of the magnetic field into account. We build local magnetic field maps using three-dimensional hexagonal block tiling. To make our approach computationally tractable we use reduced-rank Gaussian process regression in combination with a Rao–Blackwellised particle filter. We show that it is possible to obtain accurate position and orientation estimates using measurements from a smartphone, and that our approach provides a scalable magnetic SLAM algorithm in terms of both computational complexity and map storage.

Approximate Inference for Robust Gaussian Process Regression

Malte Kuß, Tobias Pfingsten, Lehel Csatò, Carl Edward Rasmussen, 2005. Max Planck Institute for Biological Cybernetics, Tübingen, Germany.

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Gaussian process (GP) priors have been successfully used in non-parametric Bayesian regression and classification models. Inference can be performed analytically only for the regression model with Gaussian noise. For all other likelihood models inference is intractable and various approximation techniques have been proposed. In recent years expectation-propagation (EP) has been developed as a general method for approximate inference. This article provides a general summary of how expectation-propagation can be used for approximate inference in Gaussian process models. Furthermore we present a case study describing its implementation for a new robust variant of Gaussian process regression. To gain further insights into the quality of the EP approximation we present experiments in which we compare to results obtained by Markov chain Monte Carlo (MCMC) sampling.

Assessing Approximate Inference for Binary Gaussian Process Classification

Malte Kuß, Carl Edward Rasmussen, 2005. (Journal of Machine Learning Research).

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Gaussian process priors can be used to define flexible, probabilistic classification models. Unfortunately exact Bayesian inference is analytically intractable and various approximation techniques have been proposed. In this work we review and compare Laplace’s method and Expectation Propagation for approximate Bayesian inference in the binary Gaussian process classification model. We present a comprehensive comparison of the approximations, their predictive performance and marginal likelihood estimates to results obtained by MCMC sampling. We explain theoretically and corroborate empirically the advantages of Expectation Propagation compared to Laplace’s method.

Assessing Approximations for Gaussian Process Classification

Malte Kuß, Carl Edward Rasmussen, April 2006. (In Advances in Neural Information Processing Systems 18). Edited by Y. Weiss, B. Schölkopf, J. Platt. Cambridge, MA, USA. Whistler, BC, Canada. The MIT Press.

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Gaussian processes are attractive models for probabilistic classification but unfortunately exact inference is analytically intractable. We compare Laplace’s method and Expectation Propagation (EP) focusing on marginal likelihood estimates and predictive performance. We explain theoretically and corroborate empirically that EP is superior to Laplace. We also compare to a sophisticated MCMC scheme and show that EP is surprisingly accurate.

Approximate Inference for the Loss-Calibrated Bayesian

Simon Lacoste-Julien, Ferenc Huszár, Zoubin Ghahramani, April 2011. (In 14th International Conference on Artificial Intelligence and Statistics). Edited by Geoff Gordon, David Dunson. Fort Lauderdale, FL, USA. Journal of Machine Learning Research.

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We consider the problem of approximate inference in the context of Bayesian decision theory. Traditional approaches focus on approximating general properties of the posterior, ignoring the decision task – and associated losses – for which the posterior could be used. We argue that this can be suboptimal and propose instead to loss-calibrate the approximate inference methods with respect to the decision task at hand. We present a general framework rooted in Bayesian decision theory to analyze approximate inference from the perspective of losses, opening up several research directions. As a first loss-calibrated approximate inference attempt, we propose an EM-like algorithm on the Bayesian posterior risk and show how it can improve a standard approach to Gaussian process classification when losses are asymmetric.

Sparse Gaussian Process Hyperparameters: Optimize or Integrate?

Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl E. Rasmussen, 2022. (In nips36).

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The kernel function and its hyperparameters are the central model selection choice in a Gaussian process [Rasmussen and Williams, 2006]. Typically, the hyperparameters of the kernel are chosen by maximising the marginal likelihood, an approach known as Type-II maximum likelihood (ML-II). However, ML-II does not account for hyperparameter uncertainty, and it is well-known that this can lead to severely biased estimates and an underestimation of predictive uncertainty. While there are several works which employ a fully Bayesian characterisation of GPs, relatively few propose such approaches for the sparse GPs paradigm. In this work we propose an algorithm for sparse Gaussian process regression which leverages MCMC to sample from the hyperparameter posterior within the variational inducing point framework of [Titsias, 2009]. This work is closely related to Hensman et al. [2015b], but side-steps the need to sample the inducing points, thereby significantly improving sampling efficiency in the Gaussian likelihood case. We compare this scheme against natural baselines in literature along with stochastic variational GPs (SVGPs) along with an extensive computational analysis.

Approximate inference for Fully Bayesian Gaussian process Regression

Vidhi Lalchand, Carl Edward Rasmussen, 2020. (In 2nd Symposium on Advances in Approximate Bayesian Inference).

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Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function. The classical approach entails maximizing the marginal likelihood yielding fixed point estimates (an approach called Type II maximum likelihood or ML-II). An alternative learning procedure is to infer the posterior over hyper-parameters in a hierarchical specication of GPs we call Fully Bayesian Gaussian Process Regression (GPR). This work considers two approximation schemes for the intractable hyperparameter posterior: 1) Hamiltonian Monte Carlo (HMC) yielding a sampling based approximation and 2) Variational Inference (VI) where the posterior over hyperparameters is approximated by a factorized Gaussian (mean-field) or a full-rank Gaussian accounting for correlations between hyperparameters. We analyse the predictive performance for fully Bayesian GPR on a range of benchmark data sets.

Generalised GPLVM with Stochastic Variational Inference

Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence, 28–30 Mar 2022. (In 25th International Conference on Artificial Intelligence and Statistics). PMLR. Proceedings of Machine Learning Research.

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Gaussian process latent variable models (GPLVM) are a flexible and non-linear approach to dimensionality reduction, extending classical Gaussian processes to an unsupervised learning context. The Bayesian incarnation of the GPLVM uses a variational framework, where the posterior over latent variables is approximated by a well-behaved variational family, a factorised Gaussian yielding a tractable lower bound. However, the non-factorisability of the lower bound prevents truly scalable inference. In this work, we study the doubly stochastic formulation of the Bayesian GPLVM model amenable with minibatch training. We show how this framework is compatible with different latent variable formulations and perform experiments to compare a suite of models. Further, we demonstrate how we can train in the presence of massively missing data and obtain high-fidelity reconstructions. We demonstrate the model’s performance by benchmarking against the canonical sparse GPLVM for high dimensional data examples.

Kernel Learning for Explainable Climate Science

Vidhi Lalchand, Kenza Tazi, Talay M Cheema, Richard E Turner, Scott Hosking, 2022. (In 16th Bayesian Modelling Applications Workshop at UAI, 2022).

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The Upper Indus Basin, Himalayas provides water for 270 million people and countless ecosystems. However, precipitation, a key component to hydrological modelling, is poorly understood in this area. A key challenge surrounding this uncertainty comes from the complex spatial-temporal distribution of precipitation across the basin. In this work we propose Gaussian processes with structured non-stationary kernels to model precipitation patterns in the UIB. Previous attempts to quantify or model precipitation in the Hindu Kush Karakoram Himalayan region have often been qualitative or include crude assumptions and simplifications which cannot be resolved at lower resolutions. This body of research also provides little to no error propagation. We account for the spatial variation in precipitation with a non-stationary Gibbs kernel parameterised with an input dependent lengthscale. This allows the posterior function samples to adapt to the varying precipitation patterns inherent in the distinct underlying topography of the Indus region. The input dependent lengthscale is governed by a latent Gaussian process with a stationary squared-exponential kernel to allow the function level hyperparameters to vary smoothly. In ablation experiments we motivate each component of the proposed kernel by demonstrating its ability to model the spatial covariance, temporal structure and joint spatio-temporal reconstruction. We benchmark our model with a stationary Gaussian process and a Deep Gaussian processes.

Sparse Spectrum Gaussian Process Regression

Miguel Lázaro-Gredilla, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Aníbal Figueiras-Vidal, June 2010. (Journal of Machine Learning Research).

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We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsify the spectral representation of the GP. This leads to a simple, practical algorithm for regression tasks. We compare the achievable trade-offs between predictive accuracy and computational requirements, and show that these are typically superior to existing state-of-the-art sparse approximations. We discuss both the weight space and function space representations, and note that the new construction implies priors over functions which are always stationary, and can approximate any covariance function in this class.

GEFCom2012 Hierarchical Load Forecasting: Gradient Boosting Machines and Gaussian Processes

James Robert Lloyd, 2013. (International Journal of Forecasting).

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This report discusses methods for forecasting hourly loads of a US utility as part of the load forecasting track of the Global Energy Forecasting Competition 2012 hosted on Kaggle. The methods described (gradient boosting machines and Gaussian processes) are generic machine learning / regression algorithms and few domain specific adjustments were made. Despite this, the algorithms were able to produce highly competitive predictions and hopefully they can inspire more refined techniques to compete with state-of-the-art load forecasting methodologies.

Representation, learning, description and criticism of probabilistic models with applications to networks, functions and relational data

James Rovert Lloyd, 2015. University of Cambridge, Department of Engineering, Cambridge, UK.

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This thesis makes contributions to a variety of aspects of probabilistic inference. When performing probabilistic inference, one must first represent one’s beliefs with a probability distribution. Specifying the details of a probability distribution can be a difficult task in many situations, but when expressing beliefs about complex data structures it may not even be apparent what form such a distribution should take. This thesis starts by demonstrating how representation theorems due to Aldous, Hoover and Kallenberg can be used to specify appropriate models for data in the form of networks. These theorems are then extended in order to reveal appropriate probability distributions for arbitrary relational data or databases. A simpler data structure to specify probability distributions for is that of functions; many probability distributions for functions have been used for centuries. We demonstrate that many of these distributions can be expressed in a common language of Gaussian process kernels constructed from a few base elements and operators. The structure of this language allows for the effective automatic construction of probabilistic models for functions. Furthermore, the formal mathematical language of kernels can be mapped neatly onto natural language allowing for automatic descriptions of the automatically constructed models. By further automating the construction of statistical models, the need to be able to effectively check or criticise these models becomes greater. This thesis demonstrates how kernel two sample tests can be used to demonstrate where a probabilistic model most disagrees with data allowing for targeted improvements to the model. In proposing a new method of model criticism this thesis also briefly discusses the philosophy of model criticism within the context of probabilistic inference.

Automatic Construction and Natural-Language Description of Nonparametric Regression Models

James Robert Lloyd, David Duvenaud, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani, July 2014. (In Association for the Advancement of Artificial Intelligence (AAAI)).

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This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural-language text. Our approach treats unknown regression functions nonparametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state-of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.

Statistical Model Criticism using Kernel Two Sample Tests

James Robert Lloyd, Zoubin Ghahramani, December 2015. (In Advances in Neural Information Processing Systems 29). Montreal, Canada.

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We propose an exploratory approach to statistical model criticism using maximum mean discrepancy (MMD) two sample tests. Typical approaches to model criticism require a practitioner to select a statistic by which to measure discrepancies between data and a statistical model. MMD two sample tests are instead constructed as an analytic maximisation over a large space of possible statistics and therefore automatically select the statistic which most shows any discrepancy. We demonstrate on synthetic data that the selected statistic, called the witness function, can be used to identify where a statistical model most misrepresents the data it was trained on. We then apply the procedure to real data where the models being assessed are restricted Boltzmann machines, deep belief networks and Gaussian process regression and demonstrate the ways in which these models fail to capture the properties of the data they are trained on.

Random function priors for exchangeable arrays with applications to graphs and relational data

James Robert Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy, December 2012. (In Advances in Neural Information Processing Systems 26). Lake Tahoe, California, USA.

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A fundamental problem in the analysis of structured relational data like graphs, networks, databases, and matrices is to extract a summary of the common structure underlying relations between individual entities. Relational data are typically encoded in the form of arrays; invariance to the ordering of rows and columns corresponds to exchangeable arrays. Results in probability theory due to Aldous, Hoover and Kallenberg show that exchangeable arrays can be represented in terms of a random measurable function which constitutes the natural model parameter in a Bayesian model. We obtain a flexible yet simple Bayesian nonparametric model by placing a Gaussian process prior on the parameter function. Efficient inference utilises elliptical slice sampling combined with a random sparse approximation to the Gaussian process. We demonstrate applications of the model to network data and clarify its relation to models in the literature, several of which emerge as special cases.

Antithetic and Monte Carlo kernel estimators for partial rankings

Maria Lomeli, Mark Rowland, Arthur Gretton, Zoubin Ghahramani, 2018. (arXiv preprint arXiv:1807.00400).

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In the modern age, rankings data is ubiquitous and it is useful for a variety of applications such as recommender systems, multi-object tracking and preference learning. However, most rankings data encountered in the real world is incomplete, which prevents the direct application of existing modelling tools for complete rankings. Our contribution is a novel way to extend kernel methods for complete rankings to partial rankings, via consistent Monte Carlo estimators for Gram matrices: matrices of kernel values between pairs of observations. We also present a novel variance reduction scheme based on an antithetic variate construction between permutations to obtain an improved estimator for the Mallows kernel. The corresponding antithetic kernel estimator has lower variance and we demonstrate empirically that it has a better performance in a variety of Machine Learning tasks. Both kernel estimators are based on extending kernel mean embeddings to the embedding of a set of full rankings consistent with an observed partial ranking. They form a computationally tractable alternative to previous approaches for partial rankings data. An overview of the existing kernels and metrics for permutations is also provided.

Gaussian Process Vine Copulas for Multivariate Dependence

David Lopez-Paz, José Miguel Hernández-Lobato, Zoubin Ghahramani, June 2013. (In 30th International Conference on Machine Learning). Atlanta, Georgia, USA.

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Copulas allow to learn marginal distributions separately from the multivariate dependence structure (copula) that links them together into a density function. Vine factorizations ease the learning of high-dimensional copulas by constructing a hierarchy of conditional bivariate copulas. However, to simplify inference, it is common to assume that each of these conditional bivariate copulas is independent from its conditioning variables. In this paper, we relax this assumption by discovering the latent functions that specify the shape of a conditional copula given its conditioning variables We learn these functions by following a Bayesian approach based on sparse Gaussian processes with expectation propagation for scalable, approximate inference. Experiments on real-world datasets show that, when modeling all conditional dependencies, we obtain better estimates of the underlying copula of the data.

On Sparse Variational methods and the Kullback-Leibler divergence between stochastic processes

Alexander G D G Matthews, James Hensman, Richard E. Turner, Zoubin Ghahramani, May 2016. (In 19th International Conference on Artificial Intelligence and Statistics). Cadiz, Spain.

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The variational framework for learning inducing variables (Titsias, 2009a) has had a large impact on the Gaussian process literature. The framework may be interpreted as minimizing a rigorously defined Kullback-Leibler divergence between the approximating and posterior processes. To our knowledge this connection has thus far gone unremarked in the literature. In this paper we give a substantial generalization of the literature on this topic. We give a new proof of the result for infinite index sets which allows inducing points that are not data points and likelihoods that depend on all function values. We then discuss augmented index sets and show that, contrary to previous works, marginal consistency of augmentation is not enough to guarantee consistency of variational inference with the original model. We then characterize an extra condition where such a guarantee is obtainable. Finally we show how our framework sheds light on interdomain sparse approximations and sparse approximations for Cox processes.

Sample-then-optimise posterior sampling for Bayesian linear models

Alexander G. D. G. Matthews, Jiri Hron, Richard E. Turner, Zoubin Ghahramani, 2017. (AABI (NeurIPS workshop)).

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In modern machine learning it is common to train models which have an extremely high intrinsic capacity. The results obtained are often i nitialization dependent, are different for disparate optimizers and in some cases have no explicit regularization. This raises difficult questions about generalization. A natural approach to questions of generalization is a Bayesian one. There is therefore a growing literature attempting to understand how Bayesian posterior inference could emerge from the complexity of modern practice, even without having such a procedure as the stated goal. In this work we consider a simple special case where exact Bayesian posterior sampling emerges from sampling (cf initialization) and then gradient descent. Specifically, for a Bayesian linear model, if we parameterize it in terms of a deterministic function of an isotropic normal prior, then the action of sampling from the prior followed by first order optimization of the squared loss will give a posterior sample. Although the assumptions are stronger than many real problems, it still exhibits the challenging properties of redundant model capacity and a lack of explicit regularizers, along with initialization and optimizer dependence. It is therefore an interesting controlled test case. Given its simplicity, the method itself may turn out to be of independent interest from our original goal.

Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs

Rowan McAllister, Carl Edward Rasmussen, December 2017. (In Advances in Neural Information Processing Systems 31). Long Beach, California.

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We present a data-efficient reinforcement learning method for continuous state-action systems under significant observation noise. Data-efficient solutions under small noise exist, such as PILCO which learns the cartpole swing-up task in 30s. PILCO evaluates policies by planning state-trajectories using a dynamics model. However, PILCO applies policies to the observed state, therefore planning in observation space. We extend PILCO with filtering to instead plan in belief space, consistent with partially observable Markov decisions process (POMDP) planning. This enables data-efficient learning under significant observation noise, outperforming more naive methods such as post-hoc application of a filter to policies optimised by the original (unfiltered) PILCO algorithm. We test our method on the cartpole swing-up task, which involves nonlinear dynamics and requires nonlinear control.

Nonlinear Modelling and Control using Gaussian Processes

Andrew McHutchon, 2014. University of Cambridge, Department of Engineering, Cambridge, UK.

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In many scientific disciplines it is often required to make predictions about how a system will behave or to deduce the correct control values to elicit a particular desired response. Efficiently solving both of these tasks relies on the construction of a model capturing the system’s operation. In the most interesting situations, the model needs to capture strongly nonlinear effects and deal with the presence of uncertainty and noise. Building models for such systems purely based on a theoretical understanding of underlying physical principles can be infeasibly complex and require a large number of simplifying assumptions. An alternative is to use a data-driven approach, which builds a model directly from observations. A powerful and principled approach to doing this is to use a Gaussian Process (GP). In this thesis we start by discussing how GPs can be applied to data sets which have noise affecting their inputs. We present the “Noisy Input GP”, which uses a simple local-linearisation to refer the input noise into heteroscedastic output noise, and compare it to other methods both theoretically and empirically. We show that this technique leads to a effective model for nonlinear functions with input and output noise. We then consider the broad topic of GP state space models for application to dynamical systems. We discuss a very wide variety of approaches for using GPs in state space models, including introducing a new method based on moment-matching, which consistently gave the best performance. We analyse the methods in some detail including providing a systematic comparison between approximate-analytic and particle methods. To our knowledge such a comparison has not been provided before in this area. Finally, we investigate an automatic control learning framework, which uses Gaussian Processes to model a system for which we wish to design a controller. Controller design for complex systems is a difficult task and thus a framework which allows an automatic design directly from data promises to be extremely useful. We demonstrate that the previously published framework cannot cope with the presence of observation noise but that the introduction of a state space model dramatically improves its performance. This contribution, along with some other suggested improvements opens the door for this framework to be used in real-world applications.

Gaussian Process Training with Input Noise

Andrew McHutchon, Carl Edward Rasmussen, 2011. (In Advances in Neural Information Processing Systems 24). Edited by J. Shawe-Taylor, R.S. Zemel, P.L. Bartlett, F. Pereira, K.Q. Weinberger. Granada, Spain. Curran Associates, Inc..

Abstract▼ URL

In standard Gaussian Process regression input locations are assumed to be noise free. We present a simple yet effective GP model for training on input points corrupted by i.i.d. Gaussian noise. To make computations tractable we use a local linear expansion about each input point. This allows the input noise to be recast as output noise proportional to the squared gradient of the GP posterior mean. The input noise hyperparameters are trained alongside other hyperparameters by the usual method of maximisation of the marginal likelihood, and allow estimation of the noise levels on each input dimension. Training uses an iterative scheme, which alternates between optimising the hyperparameters and calculating the posterior gradient. Analytic predictive moments can then be found for Gaussian distributed test points. We compare our model to others over a range of different regression problems and show that it improves over current methods.

Information-theoretic Inducing Point Placement for High-Throughput Bayesian Optimisation

Henry B. Moss, Sebastian W. Ober, Victor Picheny, 2022. (In ICML Workshop on Adaptive Experimental Design and Active Learning in the Real World (RealML)).

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Sparse Gaussian Processes are a key component of high-throughput Bayesian optimisation (BO) loops — an increasingly common setting where evaluation budgets are large and highly parallelised. By using representative subsets of the available data to build approximate posteriors, sparse models dramatically reduce the computational costs of surrogate modelling by relying on a small set of pseudo-observations, the so-called inducing points, in lieu of the full data set. However, current approaches to design inducing points are not appropriate within BO loops as they seek to reduce global uncertainty in the objective function. Thus, the high-fidelity modelling of promising and data-dense regions required for precise optimisation is sacrificed and computational resources are instead wasted on modelling areas of the space already known to be sub-optimal. Inspired by entropy-based BO methods, we propose a novel inducing point design that uses a principled information-theoretic criterion to select inducing points. By choosing inducing points to maximally reduce both global uncertainty and uncertainty in the maximum value of the objective function, we build surrogate models able to support high-precision high-throughput BO.

Adaptive, Cautious, Predictive control with Gaussian Process Priors

Roderick Murray-Smith, Daniel Sbarbaro, Carl Edward Rasmussen, Agathe Girard, August 2003. (In IFAC SYSID 2003). Edited by P. Van den Hof, B. Wahlberg, S. Weiland. (Proceedings of the 13th IFAC Symposium on System Identification). Oxford, UK. Rotterdam, The Netherlands. Elsevier Science Ltd.

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Nonparametric Gaussian Process models, a Bayesian statistics approach, are used to implement a nonlinear adaptive control law. Predictions, including propagation of the state uncertainty are made over a k-step horizon. The expected value of a quadratic cost function is minimised, over this prediction horizon, without ignoring the variance of the model predictions. The general method and its main features are illustrated on a simulation example.

The Multivariate Generalised von Mises Distribution: Inference and applications

Alexandre Khae Wu Navarro, Jes Frellsen, Richard E. Turner, January 2017. (In 31st AAAI Conference on Artificial Intelligence). Edited by S. Singh, S. Markovitch. San Francisco, CA, USA. AAAI Press.

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Circular variables arise in a multitude of data-modelling contexts ranging from robotics to the social sciences, but they have been largely overlooked by the machine learning community. This paper partially redresses this imbalance by extending some standard probabilistic modelling tools to the circular domain. First we introduce a new multivariate distribution over circular variables, called the multivariate Generalised von Mises (mGvM) distribution. This distribution can be constructed by restricting and renormalising a general multivariate Gaussian distribution to the unit hyper-torus. Previously proposed multivariate circular distributions are shown to be special cases of this construction. Second, we introduce a new probabilistic model for circular regression, that is inspired by Gaussian Processes, and a method for probabilistic principal component analysis with circular hidden variables. These models can leverage standard modelling tools (e.g. covariance functions and methods for automatic relevance determination). Third, we show that the posterior distribution in these models is a mGvM distribution which enables development of an efficient variational free-energy scheme for performing approximate inference and approximate maximum-likelihood learning.

Approximations for Binary Gaussian Process Classification

Hannes Nickisch, Carl Edward Rasmussen, October 2008. (Journal of Machine Learning Research).

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We provide a comprehensive overview of many recent algorithms for approximate inference in Gaussian process models for probabilistic binary classification. The relationships between several approaches are elucidated theoretically, and the properties of the different algorithms are corroborated by experimental results. We examine both 1) the quality of the predictive distributions and 2) the suitability of the different marginal likelihood approximations for model selection (selecting hyperparameters) and compare to a gold standard based on MCMC. Interestingly, some methods produce good predictive distributions although their marginal likelihood approximations are poor. Strong conclusions are drawn about the methods: The Expectation Propagation algorithm is almost always the method of choice unless the computational budget is very tight. We also extend existing methods in various ways, and provide unifying code implementing all approaches.

Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

Hannes Nickisch, Carl Edward Rasmussen, September 2010. (In Proceedings of the 32nd DAGM Symposium on Pattern Recognition). Darmstadt, Germany. Springer. Lecture Notes in Computer Science (LNCS). DOI: 10.1007/978-3-642-15986-2_28.

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Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets.

Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes

Sebastian W. Ober, Laurence Aitchison, 2021. (In 38th International Conference on Machine Learning).

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We consider the optimal approximate posterior over the top-layer weights in a Bayesian neural network for regression, and show that it exhibits strong dependencies on the lower-layer weights. We adapt this result to develop a correlated approximate posterior over the weights at all layers in a Bayesian neural network. We extend this approach to deep Gaussian processes, unifying inference in the two model classes. Our approximate posterior uses learned “global” inducing points, which are defined only at the input layer and propagated through the network to obtain inducing inputs at subsequent layers. By contrast, standard “local”, inducing point methods from the deep Gaussian process literature optimise a separate set of inducing inputs at every layer, and thus do not model correlations across layers. Our method gives state-of-the-art performance for a variational Bayesian method, without data augmentation or tempering, on CIFAR-10 of 86.7%, which is comparable to SGMCMC without tempering but with data augmentation (88% in Wenzel et al. 2020).

A variational approximate posterior for the deep Wishart process

Sebastian W. Ober, Laurence Aitchison, 2021. (In Advances in Neural Information Processing Systems 34).

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Recent work introduced deep kernel processes as an entirely kernel-based alternative to NNs (Aitchison et al. 2020). Deep kernel processes flexibly learn good top-layer representations by alternately sampling the kernel from a distribution over positive semi-definite matrices and performing nonlinear transformations. A particular deep kernel process, the deep Wishart process (DWP), is of particular interest because its prior can be made equivalent to deep Gaussian process (DGP) priors for kernels that can be expressed entirely in terms of Gram matrices. However, inference in DWPs has not yet been possible due to the lack of sufficiently flexible distributions over positive semi-definite matrices. Here, we give a novel approach to obtaining flexible distributions over positive semi-definite matrices by generalising the Bartlett decomposition of the Wishart probability density. We use this new distribution to develop an approximate posterior for the DWP that includes dependency across layers. We develop a doubly-stochastic inducing-point inference scheme for the DWP and show experimentally that inference in the DWP can improve performance over doing inference in a DGP with the equivalent prior.

The promises and pitfalls of deep kernel learning

Sebastian W. Ober, Carl Edward Rasmussen, Mark van der Wilk, 2021. (In 37th Conference on Uncertainty in Artificial Intelligence).

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Deep kernel learning (DKL) and related techniques aim to combine the representational power of neural networks with the reliable uncertainty estimates of Gaussian processes. One crucial aspect of these models is an expectation that, because they are treated as Gaussian process models optimized using the marginal likelihood, they are protected from overfitting. However, we identify situations where this is not the case. We explore this behavior, explain its origins and consider how it applies to real datasets. Through careful experimentation on the UCI, CIFAR-10, and the UTKFace datasets, we find that the overfitting from overparameterized maximum marginal likelihood, in which the model is “somewhat Bayesian”, can in certain scenarios be worse than that from not being Bayesian at all. We explain how and when DKL can still be successful by investigating optimization dynamics. We also find that failures of DKL can be rectified by a fully Bayesian treatment, which leads to the desired performance improvements over standard neural networks and Gaussian processes.

Active Learning of Model Evidence Using Bayesian Quadrature

Michael A. Osborne, David Duvenaud, Roman Garnett, Carl Edward Rasmussen, Stephen J. Roberts, Zoubin Ghahramani, December 2012. (In Advances in Neural Information Processing Systems 25). Lake Tahoe, California, USA.

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Numerical integration is a key component of many problems in scientific computing, statistical modelling, and machine learning. Bayesian Quadrature is a model-based method for numerical integration which, relative to standard Monte Carlo methods, offers increased sample efficiency and a more robust estimate of the uncertainty in the estimated integral. We propose a novel Bayesian Quadrature approach for numerical integration when the integrand is non-negative, such as the case of computing the marginal likelihood, predictive distribution, or normalising constant of a probabilistic model. Our approach approximately marginalises the quadrature model’s hyperparameters in closed form, and introduces an active learning scheme to optimally select function evaluations, as opposed to using Monte Carlo samples. We demonstrate our method on both a number of synthetic benchmarks and a real scientific problem from astronomy.

Propagation of Uncertainty in Bayesian Kernel Models - Application to Multiple-Step Ahead Forecasting

Joaquin Quiñonero-Candela, Agathe Girard, Jan Larsen, Carl Edward Rasmussen, April 2003. (In ICASSP 2003). (IEEE International Conference on Acoustics, Speech and Signal Processing). Hong Kong.

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The object of Bayesian modelling is the predictive distribution, which in a forecasting scenario enables improved estimates of forecasted values and their uncertainties. In this paper we focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models such as the Gaussian Process and the Relevance Vector Machine. We derive novel analytic expressions for the predictive mean and variance for Gaussian kernel shapes under the assumption of a Gaussian input distribution in the static case, and of a recursive Gaussian predictive density in iterative forecasting. The capability of the method is demonstrated for forecasting of time-series and compared to approximate methods.

Prediction at an uncertain input for Gaussian processes and Relevance Vector Machines Application to multiple-step ahead time-series prediction

Joaquin Quiñonero-Candela, Agathe Girard, Carl Edward Rasmussen, 2003. Instititute for Mathemetical Modelling, DTU,

URL

Comment: techreport

A Unifying View of Sparse Approximate Gaussian Process Regression

Joaquin Quiñonero-Candela, Carl Edward Rasmussen, 2005. (Journal of Machine Learning Research).

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We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints.

Analysis of Some Methods for Reduced Rank Gaussian Process Regression

Joaquin Quiñonero-Candela, Carl Edward Rasmussen, 2005. (In Switching and Learning in Feedback Systems). Edited by R. Murray-Smith, R. Shorten. Berlin, Heidelberg. Springer.

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While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational complexity makes them impractical when the size of the training set exceeds a few thousand cases. This has motivated the recent proliferation of a number of cost-effective approximations to GPs, both for classification and for regression. In this paper we analyze one popular approximation to GPs for regression: the reduced rank approximation. While generally GPs are equivalent to infinite linear models, we show that Reduced Rank Gaussian Processes (RRGPs) are equivalent to finite sparse linear models. We also introduce the concept of degenerate GPs and show that they correspond to inappropriate priors. We show how to modify the RRGP to prevent it from being degenerate at test time. Training RRGPs consists both in learning the covariance function hyperparameters and the support set. We propose a method for learning hyperparameters for a given support set. We also review the Sparse Greedy GP (SGGP) approximation (Somla and Bartlett, 2001), which is a way of learning the support set for given hyperparameters based on approximating the posterior. We propose an alternative method to the SGGP that has better generalization capabilities. Finally we make experiments to compare the different ways of training a RRGP. We provide some Matlab code for learning RRGPs.

Approximation Methods for Gaussian Process Regression

Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Christopher K. I. Williams, September 2007. (In Large-Scale Kernel Machines). Edited by L. Bottou, O. Chapelle, D. DeCoste, J. Weston. Cambridge, MA, USA. The MIT Press. Neural Information Processing.

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A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following Quiñonero-Candela and Rasmussen (2005), and a brief review of approximate matrix-vector multiplication methods.

Comment: book

Gaussian Processes to Speed up Hybrid Monte Carlo for Expensive Bayesian Integrals

Carl Edward Rasmussen, 2003. (In Bayesian Statistics 7). Edited by J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, M. West. Oxford University Press.

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Hybrid Monte Carlo (HMC) is often the method of choice for computing Bayesian integrals that are not analytically tractable. However the success of this method may require a very large number of evaluations of the (un-normalized) posterior and its partial derivatives. In situations where the posterior is computationally costly to evaluate, this may lead to an unacceptable computational load for HMC. I propose to use a Gaussian Process model of the (log of the) posterior for most of the computations required by HMC. Within this scheme only occasional evaluation of the actual posterior is required to guarantee that the samples generated have exactly the desired distribution, even if the GP model is somewhat inaccurate. The method is demonstrated on a 10 dimensional problem, where 200 evaluations suffice for the generation of 100 roughly independent points from the posterior. Thus, the proposed scheme allows Bayesian treatment of models with posteriors that are computationally demanding, such as models involving computer simulation.

Gaussian Processes in Machine Learning

Carl Edward Rasmussen, 2004. (In Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2 - 14, 2003, Tübingen, Germany, August 4 - 16, 2003, Revised Lectures). Edited by Olivier Bousquet, Ulrike von Luxburg, Gunnar Rätsch. Heidelberg. Springer-Verlag. Lecture Notes in Computer Science (LNCS).

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We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.

Comment: Copyright by Springer, springerlink

Evaluation of Gaussian Processes and other Methods for non-linear Regression

Carl Edward Rasmussen, 1996. University of Toronto, Department of Computer Science, Toronto, CANADA.

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This thesis develops two Bayesian learning methods relying on Gaussian processes and a rigorous statistical approach for evaluating such methods. In these experimental designs the sources of uncertainty in the estimated generalisation performances due to both variation in training and test sets are accounted for. The framework allows for estimation of generalisation performance as well as statistical tests of significance for pairwise comparisons. Two experimental designs are recommended and supported by the DELVE software environment. Two new non-parametric Bayesian learning methods relying on Gaussian process priors over functions are developed. These priors are controlled by hyperparameters which set the characteristic length scale for each input dimension. In the simplest method, these parameters are fit from the data using optimization. In the second, fully Bayesian method, a Markov chain Monte Carlo technique is used to integrate over the hyperparameters. One advantage of these Gaussian process methods is that the priors and hyperparameters of the trained models are easy to interpret. The Gaussian process methods are benchmarked against several other methods, on regression tasks using both real data and data generated from realistic simulations. The experiments show that small datasets are unsuitable for benchmarking purposes because the uncertainties in performance measurements are large. A second set of experiments provide strong evidence that the bagging procedure is advantageous for the Multivariate Adaptive Regression Splines (MARS) method. The simulated datasets have controlled characteristics which make them useful for understanding the relationship between properties of the dataset and the performance of different methods. The dependency of the performance on available computation time is also investigated. It is shown that a Bayesian approach to learning in multi-layer perceptron neural networks achieves better performance than the commonly used early stopping procedure, even for reasonably short amounts of computation time. The Gaussian process methods are shown to consistently outperform the more conventional methods.

Infinite Mixtures of Gaussian Process Experts

Carl Edward Rasmussen, Zoubin Ghahramani, December 2002. (In Advances in Neural Information Processing Systems 14). Edited by T. G. Dietterich, S. Becker, Z. Ghahramani. Cambridge, MA, USA. The MIT Press.

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We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Using an input-dependent adaptation of the Dirichlet Process, we implement a gating network for an infinite number of Experts. Inference in this model may be done efficiently using a Markov Chain relying on Gibbs sampling. The model allows the effective covariance function to vary with the inputs, and may handle large datasets — thus potentially overcoming two of the biggest hurdles with GP models. Simulations show the viability of this approach.

Bayesian Monte Carlo

Carl Edward Rasmussen, Zoubin Ghahramani, December 2003. (In Advances in Neural Information Processing Systems 15). Edited by S. Becker, S. Thrun, K. Obermayer. Cambridge, MA, USA. The MIT Press.

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We investigate Bayesian alternatives to classical Monte Carlo methods for evaluating integrals. Bayesian Monte Carlo (BMC) allows the incorporation of prior knowledge, such as smoothness of the integrand, into the estimation. In a simple problem we show that this outperforms any classical importance sampling method. We also attempt more challenging multidimensional integrals involved in computing marginal likelihoods of statistical models (a.k.a. partition functions and model evidences). We find that Bayesian Monte Carlo outperformed Annealed Importance Sampling, although for very high dimensional problems or problems with massive multimodality BMC may be less adequate. One advantage of the Bayesian approach to Monte Carlo is that samples can be drawn from any distribution. This allows for the possibility of active design of sample points so as to maximise information gain.

Gaussian Processes for Machine Learning (GPML) Toolbox

Carl Edward Rasmussen, Hannes Nickisch, December 2010. (Journal of Machine Learning Research).

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The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library of simple mean and covariance functions and mechanisms to compose more complex ones. Several likelihood functions are supported including Gaussian and heavy-tailed for regression as well as others suitable for classification. Finally, a range of inference methods is provided, including exact and variational inference, Expectation Propagation, and Laplace’s method dealing with non-Gaussian likelihoods and FITC for dealing with large regression tasks.

Comment: Toolbox avaiable from here. Implements algorithms from Rasmussen and Williams, 2006.

Healing the Relevance Vector Machine through Augmentation

Carl Edward Rasmussen, Joaquin Quiñonero-Candela, 2005. (In 22nd International Conference on Machine Learning). Edited by L. De Raedt, S. Wrobel. Bonn, Germany.

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The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties have the unintuitive property, that they get smaller the further you move away from the training cases. We give a thorough analysis. Inspired by the analogy to non-degenerate Gaussian Processes, we suggest augmentation to solve the problem. The purpose of the resulting model, RVM, is primarily to corroborate the theoretical and experimental analysis. Although RVM could be used in practical applications, it is no longer a truly sparse model. Experiments show that sparsity comes at the expense of worse predictive distributions.

Gaussian Processes for Machine Learning

Carl Edward Rasmussen, Christopher K. I. Williams, 2006. The MIT Press.

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Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.

Comment: Winner of the 2009 DeGroot Prize. Book web page, chapters and entire book pdf. GPML Toolbox.

The Gaussian Process Autoregressive Regression Model (GPAR)

James Requeima, William Tebbutt, Wessel Bruinsma, Richard E. Turner, 2019. (In 22nd International Conference on Artificial Intelligence and Statistics). Proceedings of Machine Learning Research.

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Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically yields models that are computationally demanding and have limited representational power. We present the Gaussian Process Autoregressive Regression (GPAR) model, a scalable multi-output GP model that is able to capture nonlinear, possibly input-varying, dependencies between outputs in a simple and tractable way: the product rule is used to decompose the joint distribution over the outputs into a set of conditionals, each of which is modelled by a standard GP. GPAR’s efficacy is demonstrated on a variety of synthetic and real-world problems, outperforming existing GP models and achieving state-of-the-art performance on established benchmarks.

Scalable Inference for Structured Gaussian Process Models

Yunus Saatçi, 2011. University of Cambridge, Department of Engineering, Cambridge, UK.

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The generic inference and learning algorithm for Gaussian Process (GP) regression has O(N3) runtime and O(N2) memory complexity, where N is the number of observations in the dataset. Given the computational resources available to a present-day workstation, this implies that GP regression simply cannot be run on large datasets. The need to use non- Gaussian likelihood functions for tasks such as classification adds even more to the computational burden involved. The majority of algorithms designed to improve the scaling of GPs are founded on the idea of approximating the true covariance matrix, which is usually of rank N, with a matrix of rank P, where P<<N. Typically, the true training set is replaced with a smaller, representative (pseudo-) training set such that a specific measure of information loss is minimized. These algorithms typically attain O(P2N) runtime and O(PN) space complexity. They are also general in the sense that they are designed to work with any covariance function. In essence, they trade off accuracy with computational complexity. The central contribution of this thesis is to improve scaling instead by exploiting any structure that is present in the covariance matrices generated by particular covariance functions. Instead of settling for a kernel-independent accuracy/complexity trade off, as is done in much the literature, we often obtain accuracies close to, or exactly equal to the full GP model at a fraction of the computational cost. We define a structured GP as any GP model that is endowed with a kernel which produces structured covariance matrices. A trivial example of a structured GP is one with the linear regression kernel. In this case, given inputs living in RD, the covariance matrices generated have rank D – this results in significant computational gains in the usual case where D<<N. Another case arises when a stationary kernel is evaluated on equispaced, scalar inputs. This results in Toeplitz covariance matrices and all necessary computations can be carried out exactly in O(N log N). This thesis studies four more types of structured GP. First, we comprehensively review the case of kernels corresponding to Gauss-Markov processes evaluated on scalar inputs. Using state-space models we show how (generalised) regression (including hyperparameter learning) can be performed in O(N log N) runtime and O(N) space. Secondly, we study the case where we introduce block structure into the covariance matrix of a GP time-series model by assuming a particular form of nonstationarity a priori. Third, we extend the efficiency of scalar Gauss-Markov processes to higher-dimensional input spaces by assuming additivity. We illustrate the connections between the classical backfitting algorithm and approximate Bayesian inference techniques including Gibbs sampling and variational Bayes. We also show that it is possible to relax the rather strong assumption of additivity without sacrificing O(N log N) complexity, by means of a projection-pursuit style GP regression model. Finally, we study the properties of a GP model with a tensor product kernel evaluated on a multivariate grid of inputs locations. We show that for an arbitrary (regular or irregular) grid the resulting covariance matrices are Kronecker and full GP regression can be implemented in O(N) time and memory usage. We illustrate the power of these methods on several real-world regression datasets which satisfy the assumptions inherent in the structured GP employed. In many cases we obtain performance comparable to the generic GP algorithm. We also analyse the performance degradation when these assumptions are not met, and in several cases show that it is comparable to that observed for sparse GP methods. We provide similar results for regression tasks with non-Gaussian likelihoods, an extension rarely addressed by sparse GP techniques.

Gaussian Process Change Point Models

Yunus Saatçi, Ryan Turner, Carl Edward Rasmussen, June 2010. (In 27th International Conference on Machine Learning). Haifa, Israel.

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We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets.

Comment: poster, slides.

Probabilistic ODE Solvers with Runge-Kutta Means

Michael Schober, David Duvenaud, Philipp Hennig, June 2014. (arXiv preprint arXiv:1406.2582).

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Runge-Kutta methods are the classic family of solvers for ordinary differential equations (ODEs), and the basis for the state-of-the-art. Like most numerical methods, they return point estimates. We construct a family of probabilistic numerical methods that instead return a Gauss-Markov process defining a probability distribution over the ODE solution. In contrast to prior work, we construct this family such that posterior means match the outputs of the Runge-Kutta family exactly, thus inheriting their proven good properties. Remaining degrees of freedom not identified by the match to Runge-Kutta are chosen such that the posterior probability measure fits the observed structure of the ODE. Our results shed light on the structure of Runge-Kutta solvers from a new direction, provide a richer, probabilistic output, have low computational cost, and raise new research questions.

Kernel adaptive Metropolis-Hastings

Dino Sejdinovic, Heiko Strathmann, Maria Lomeli, Christophe Andrieu, Arthur Gretton, June 2012. (In 31st International Conference on Machine Learning). Beijing, China.

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A Kernel Adaptive Metropolis-Hastings algo- rithm is introduced, for the purpose of sampling from a target distribution with strongly nonlin- ear support. The algorithm embeds the trajec- tory of the Markov chain into a reproducing ker- nel Hilbert space (RKHS), such that the fea- ture space covariance of the samples informs the choice of proposal. The procedure is com- putationally efficient and straightforward to im- plement, since the RKHS moves can be inte- grated out analytically: our proposal distribu- tion in the original space is a normal distribution whose mean and covariance depend on where the current sample lies in the support of the tar- get distribution, and adapts to its local covari- ance structure. Furthermore, the procedure re- quires neither gradients nor any other higher or- der information about the target, making it par- ticularly attractive for contexts such as Pseudo- Marginal MCMC. Kernel Adaptive Metropolis- Hastings outperforms competing fixed and adap- tive samplers on multivariate, highly nonlinear target distributions, arising in both real-world and synthetic examples.

Student-t Processes as Alternatives to Gaussian Processes

Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani, 2014. (In AISTATS). JMLR.org. JMLR Proceedings.

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We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the covariance kernel of a Gaussian process model. We show surprising equivalences between different hierarchical Gaussian process models leading to Student-t processes, and derive a new sampling scheme for the inverse Wishart process, which helps elucidate these equivalences. Overall, we show that a Student-t process can retain the attractive properties of a Gaussian process – a nonparametric representation, analytic marginal and predictive distributions, and easy model selection through covariance kernels – but has enhanced flexibility, and predictive covariances that, unlike a Gaussian process, explicitly depend on the values of training observations. We verify empirically that a Student-t process is especially useful in situations where there are changes in covariance structure, or in applications like Bayesian optimization, where accurate predictive covariances are critical for good performance. These advantages come at no additional computational cost over Gaussian processes.

Kernel Identification Through Transformers

Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl Edward Rasmussen, 2021. (In Advances in Neural Information Processing Systems 34).

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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. This work addresses the challenge of constructing custom kernel functions for high-dimensional GP regression models. Drawing inspiration from recent progress in deep learning, we introduce a novel approach named KITT: Kernel Identification Through Transformers. KITT exploits a transformer-based architecture to generate kernel recommendations in under 0.1 seconds, which is several orders of magnitude faster than conventional kernel search algorithms. We train our model using synthetic data generated from priors over a vocabulary of known kernels. By exploiting the nature of the self-attention mechanism, KITT is able to process datasets with inputs of arbitrary dimension. We demonstrate that kernels chosen by KITT yield strong performance over a diverse collection of regression benchmarks.

Marginalised Gaussian Processes with Nested Sampling

Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen, 2021. (In Advances in Neural Information Processing Systems 34). Curran Associates, Inc..

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Gaussian Process models are a rich distribution over functions with inductive biases controlled by a kernel function. Learning occurs through optimisation of the kernel hyperparameters using the marginal likelihood as the objective. This work proposes nested sampling as a means of marginalising kernel hyperparameters, because it is a technique that is well-suited to exploring complex, multi-modal distributions. We benchmark against Hamiltonian Monte Carlo on time-series and two-dimensional regression tasks, finding that a principled approach to quantifying hyperparameter uncertainty substantially improves the quality of prediction intervals.

Consistent Kernel Mean Estimation for Functions of Random Variables

Carl-Johann Simon-Gabriel, Adam Ścibior, Ilya Tolstikhin, Bernhard Schölkopf, 2016. (In Advances in Neural Information Processing Systems 30).

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We provide a theoretical foundation for non-parametric estimation of functions of random variables using kernel mean embeddings. We show that for any continuous function f, consistent estimators of the mean embedding of a random variable X lead to consistent estimators of the mean embedding of f(X). For Matérn kernels and sufficiently smooth functions we also provide rates of convergence. Our results extend to functions of multiple random variables. If the variables are dependent, we require an estimator of the mean embedding of their joint distribution as a starting point; if they are independent, it is sufficient to have separate estimators of the mean embeddings of their marginal distributions. In either case, our results cover both mean embeddings based on i.i.d. samples as well as “reduced set” expansions in terms of dependent expansion points. The latter serves as a justification for using such expansions to limit memory resources when applying the approach as a basis for probabilistic programming.

Learning Depth From Stereo

Fabian Sinz, Joaquin Quiñonero-Candela, Gökhan H. Bakir, Carl Edward Rasmussen, Matthias O. Franz, 09 2004. (In 26th DAGM Symposium). Edited by C. E. Rasmussen, H. H. Bülthoff, B. Schölkopf, M. A. Giese. (Pattern Recognition: Proceedings of the 26th DAGM Symposium). Berlin, Germany. Tübingen, Germany. Springer. Lecture Notes in Computer Science (LNCS).

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We compare two approaches to the problem of estimating the depth of a point in space from observing its image position in two different cameras: 1. The classical photogrammetric approach explicitly models the two cameras and estimates their intrinsic and extrinsic parameters using a tedious calibration procedure; 2. A generic machine learning approach where the mapping from image to spatial coordinates is directly approximated by a Gaussian Process regression. Our results show that the generic learning approach, in addition to simplifying the procedure of calibration, can lead to higher depth accuracies than classical calibration although no specific domain knowledge is used.

Sparse Gaussian Processes using Pseudo-inputs

Edward Snelson, Zoubin Ghahramani, 2006. (In Advances in Neural Information Processing Systems 18). Edited by Y. Weiss, B. Schölkopf, J. Platt. Cambridge, MA. The MIT Press.

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We present a new Gaussian process (GP) regression model whose covariance is parameterized by the the locations of M pseudo-input points, which we learn by a gradient based optimization. We take M<<N, where N is the number of real data points, and hence obtain a sparse regression method which has O(NM2) training cost and O(M2) prediction cost per test case. We also find hyperparameters of the covariance function in the same joint optimization. The method can be viewed as a Bayesian regression model with particular input dependent noise. The method turns out to be closely related to several other sparse GP approaches, and we discuss the relation in detail. We finally demonstrate its performance on some large data sets, and make a direct comparison to other sparse GP methods. We show that our method can match full GP performance with small M, i.e. very sparse solutions, and it significantly outperforms other approaches in this regime.

Variable noise and dimensionality reduction for sparse Gaussian processes

Edward Snelson, Zoubin Ghahramani, 2006. (In 22nd Conference on Uncertainty in Artificial Intelligence). Edited by R. Dechter, T. S. Richardson. AUAI Press.

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The sparse pseudo-input Gaussian process (SPGP) is a new approximation method for speeding up GP regression in the case of a large number of data points N. The approximation is controlled by the gradient optimization of a small set of M pseudo-inputs, thereby reducing complexity from O(N3) to O(NM2). One limitation of the SPGP is that this optimization space becomes impractically big for high dimensional data sets. This paper addresses this limitation by performing automatic dimensionality reduction. A projection of the input space to a low dimensional space is learned in a supervised manner, alongside the pseudo-inputs, which now live in this reduced space. The paper also investigates the suitability of the SPGP for modeling data with input-dependent noise. A further extension of the model is made to make it even more powerful in this regard - we learn an uncertainty parameter for each pseudo-input. The combination of sparsity, reduced dimension, and input-dependent noise makes it possible to apply GPs to much larger and more complex data sets than was previously practical. We demonstrate the benefits of these methods on several synthetic and real world problems.

Local and global sparse Gaussian process approximations

Edward Snelson, Zoubin Ghahramani, 2007. (In 11th International Conference on Artificial Intelligence and Statistics). Edited by M. Meila, X. Shen. Omnipress.

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Gaussian process (GP) models are flexible probabilistic nonparametric models for regression, classification and other tasks. Unfortunately they suffer from computational intractability for large data sets. Over the past decade there have been many different approximations developed to reduce this cost. Most of these can be termed global approximations, in that they try to summarize all the training data via a small set of support points. A different approach is that of local regression, where many local experts account for their own part of space. In this paper we start by investigating the regimes in which these different approaches work well or fail. We then proceed to develop a new sparse GP approximation which is a combination of both the global and local approaches. Theoretically we show that it is derived as a natural extension of the framework developed by Quiñonero-Candela and Rasmussen for sparse GP approximations. We demonstrate the benefits of the combined approximation on some 1D examples for illustration, and on some large real-world data sets.

Warped Gaussian Processes

Edward Snelson, Carl Edward Rasmussen, Zoubin Ghahramani, December 2004. (In Advances in Neural Information Processing Systems 16). Edited by S. Thrun, L. Saul, B. Schölkopf. Cambridge, MA, USA. The MIT Press. ISBN: 0-262-20152-6.

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We generalise the Gaussian process (GP) framework for regression by learning a nonlinear transformation of the GP outputs. This allows for non-Gaussian processes and non-Gaussian noise. The learning algorithm chooses a nonlinear transformation such that transformed data is well-modelled by a GP. This can be seen as including a preprocessing transformation as an integral part of the probabilistic modelling problem, rather than as an ad-hoc step. We demonstrate on several real regression problems that learning the transformation can lead to significantly better performance than using a regular GP, or a GP with a fixed transformation.

Derivative observations in Gaussian Process models of dynamic systems

Ercan Solak, Roderick Murray-Smith, William E. Leithead, Douglas Leith, Carl Edward Rasmussen, December 2003. (In Advances in Neural Information Processing Systems 15). Edited by S. Becker, S. Thrun, K. Obermayer. Cambridge, MA, USA. The MIT Press.

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Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identification of nonlinear dynamic systems from experimental data. 1) It allows us to combine derivative information, and associated uncertainty with normal function observations into the learning and inference process. This derivative information can be in the form of priors specified by an expert or identified from perturbation data close to equilibrium. 2) It allows a seamless fusion of multiple local linear models in a consistent manner, inferring consistent models and ensuring that integrability constraints are met. 3) It improves dramatically the computational efficiency of Gaussian process models for dynamic system identification, by summarising large quantities of near-equilibrium data by a handful of linearisations, reducing the training set size - traditionally a problem for Gaussian process models.

Advances in Software and Spatio-Temporal Modelling with Gaussian Processes

Will Tebbutt, 2022. University of Cambridge, Department of Engineering,

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This thesis concerns the use of Gaussian processes (GPs) as distributions over unknown functions in Machine Learning and probabilistic modeling. GPs have been found to have utility in a wide range of applications owing to their flexibility, interpretability, and tractability. I advance their use in three directions. Firstly, the abstractions upon which software is built for their use in practice. In modern GP software libraries such as GPML, GPy, GPflow, and GPyTorch, the kernel is undoubtedly the dominant abstraction. While it remains highly successful it of course has limitations, and I propose to address some of these through a complementary abstraction: affine transformations of GPs. Specifically I show how a collection of GPs, and affine transformations thereof, can themselves be treated as a single GP. This in turn leads to a design for software, including exact and approximate inference algorithms. I demonstrate the utility of this software through a collection of worked examples, focussing on models which are more cleanly and easily expressed using this new software. Secondly, I develop a new scalable approximate inference algorithm for a class of GPs commonly utilised in spatio-temporal problems. This is a setting in which GPs excel, for example enabling the incorporation of important inductive biases, and observations made at arbitrary points in time and space. However, the computation required to perform exact inference and learning in GPs scales cubically in the number of observations, necessitating approximation, to which end I combine two important complementary classes of approximation: pseudo-point and Markovian. The key contribution is the insight that a simple and useful way to combine them turns out to be well-justified. This resolves an open question in the literature, provides new insight into existing work, and a new family of approximations. The efficacy of an important member of this family is demonstrated empirically. Finally I develop a GP model and associated approximate inference techniques for the prediction of sea surface temperatures (SSTs) on decadal time scales, which are relevant when taking planning decisions which consider resilience to climate change. There remains a large degree of uncertainty as to the state of the climate on such time scales, but it is thought to be possible to reduce this by exploiting the predictability of natural variability in the climate. The developed GP-based model incorporates a key assumption used by the existing statistical models employed for decadal prediction, thus retaining a valuable inductive bias, while offering several advantages. Amongst these is the lack of need for spatial aggregation of data, which is especially relevant when data are sparse, as is the case with historical ocean SST data. In summary, this thesis contributes to the practical use of GPs through a set of abstractions that are useful in the design of software, algorithms for approximate inference in spatial-temporal settings, and their use in decadal climate prediction.

Combining pseudo-point and state space approximations for sum-separable Gaussian Processes

Will Tebbutt, Arno Solin, Richard E. Turner, 2021. (In Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence). Edited by Cassio de Campos, Marloes H. Maathuis. PMLR. Proceedings of Machine Learning Research.

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Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temporal modelling problems such as those in climate science and epidemiology. However, existing GP approximations do not simultaneously support large numbers of off-the-grid spatial data-points and long time-series which is a hallmark of many applications. Pseudo-point approximations, one of the gold-standard methods for scaling GPs to large data sets, are well suited for handling off-the-grid spatial data. However, they cannot handle long temporal observation horizons effectively reverting to cubic computational scaling in the time dimension. State space GP approximations are well suited to handling temporal data, if the temporal GP prior admits a Markov form, leading to linear complexity in the number of temporal observations, but have a cubic spatial cost and cannot handle off-the-grid spatial data. In this work we show that there is a simple and elegant way to combine pseudo-point methods with the state space GP approximation framework to get the best of both worlds. The approach hinges on a surprising conditional independence property which applies to space–time separable GPs. We demonstrate empirically that the combined approach is more scalable and applicable to a greater range of spatio-temporal problems than either method on its own.

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees

Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge, 2022. (arXiv).

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As Gaussian processes mature, they are increasingly being deployed as part of larger machine learning and decision-making systems, for instance in geospatial modeling, Bayesian optimization, or in latent Gaussian models. Within a system, the Gaussian process model needs to perform in a stable and reliable manner to ensure it interacts correctly with other parts the system. In this work, we study the numerical stability of scalable sparse approximations based on inducing points. We derive sufficient and in certain cases necessary conditions on the inducing points for the computations performed to be numerically stable. For low-dimensional tasks such as geospatial modeling, we propose an automated method for computing inducing points satisfying these conditions. This is done via a modification of the cover tree data structure, which is of independent interest. We additionally propose an alternative sparse approximation for regression with a Gaussian likelihood which trades off a small amount of performance to further improve stability. We evaluate the proposed techniques on a number of examples, showing that, in geospatial settings, sparse approximations with guaranteed numerical stability often perform comparably to those without.

Learning Stationary Time Series using Gaussian Process with Nonparametric Kernels

Felipe Tobar, Thang D. Bui, Richard E. Turner, Dec 2015. (In Advances in Neural Information Processing Systems 29). Montréal CANADA.

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We introduce the Gaussian Process Convolution Model (GPCM), a two-stage nonparametric generative procedure to model stationary signals as the convolution between a continuous-time white-noise process and a continuous-time linear filter drawn from Gaussian process. The GPCM is a continuous-time nonparametricwindow moving average process and, conditionally, is itself a Gaussian process with a nonparametric kernel defined in a probabilistic fashion. The generative model can be equivalently considered in the frequency domain, where the power spectral density of the signal is specified using a Gaussian process. One of the main contributions of the paper is to develop a novel variational freeenergy approach based on inter-domain inducing variables that efficiently learns the continuous-time linear filter and infers the driving white-noise process. In turn, this scheme provides closed-form probabilistic estimates of the covariance kernel and the noise-free signal both in denoising and prediction scenarios. Additionally, the variational inference procedure provides closed-form expressions for the approximate posterior of the spectral density given the observed data, leading to new Bayesian nonparametric approaches to spectrum estimation. The proposed GPCM is validated using synthetic and real-world signals.

Unsupervised State-Space Modeling Using Reproducing Kernels

Felipe Tobar, Petar M. Djurić, Danilo P. Mandic, 2015. (IEEE Transactions on Signal Processing).

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A novel framework for the design of state-space models (SSMs) is proposed whereby the state-transition function of the model is parametrized using reproducing kernels. The nature of SSMs requires learning a latent function that resides in the state space and for which input-output sample pairs are not available, thus prohibiting the use of gradient-based supervised kernel learning. To this end, we then propose to learn the mixing weights of the kernel estimate by sampling from their posterior density using Monte Carlo methods. We first introduce an offline version of the proposed algorithm, followed by an online version which performs inference on both the parameters and the hidden state through particle filtering. The accuracy of the estimation of the state-transition function is first validated on synthetic data. Next, we show that the proposed algorithm outperforms kernel adaptive filters in the prediction of real-world time series, while also providing probabilistic estimates, a key advantage over standard methods.

High-Dimensional Kernel Regression: A Guide for Practitioners

Felipe Tobar, Danilo P. Mandic, 2015. (In Trends in Digital Signal Processing: A Festschrift in Honour of A.G. Constantinides). Edited by Y. C. Lim, H. K. Kwan, W.-C. Siu. CRC Press.

Design of Positive-Definite Quaternion Kernels

Felipe Tobar, Danilo P. Mandic, 2015. (IEEE Signal Processing Letters).

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Quaternion reproducing kernel Hilbert spaces (QRKHS) have been proposed recently and provide a high-dimensional feature space (alternative to the real-valued multikernel approach) for general kernel-learning applications. The current challenge within quaternion-kernel learning is the lack of general quaternion-valued kernels, which are necessary to exploit the full advantages of the QRKHS theory in real-world problems. This letter proposes a novel way to design quaternion-valued kernels, this is achieved by transforming three complex kernels into quaternion ones and then combining their real and imaginary parts. Building on this general construction, our emphasis is on a new quaternion kernel of polynomial features, which is assessed in the prediction of bodysensor networks applications.

Modelling of Complex Signals using Gaussian Processes

Felipe Tobar, Richard E. Turner, 2015. (In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)).

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In complex-valued signal processing, estimation algorithms require complete knowledge (or accurate estimation) of the second order statistics, this makes Gaussian processes (GP) well suited for modelling complex signals, as they are designed in terms of covariance functions. Dealing with bivariate signals using GPs require four covariance matrices, or equivalently, two complex matrices. We propose a GP-based approach for modelling complex signals, whereby the second-order statistics are learnt through maximum likelihood; in particular, the complex GP approach allows for circularity coefficient estimation in a robust manner when the observed signal is corrupted by (circular) white noise. The proposed model is validated using climate signals, for both circular and noncircular cases. The results obtained open new possibilities for collaboration between the complex signal processing and Gaussian processes communities towards an appealing representation and statistical description of bivariate signals.

Deep Structured Mixtures of Gaussian Processes

Martin Trapp, Robert Peharz, Franz Pernkopf, Carl Edward Rasmussen, August 2020. (In 23rd International Conference on Artificial Intelligence and Statistics). Online.

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Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs. In order to improve scalability of GPs, approximate posterior inference is frequently employed, where a prominent class of approximation techniques is based on local GP experts. However, local-expert techniques proposed so far are either not well-principled, come with limited approximation guarantees, or lead to intractable models. In this paper, we introduce deep structured mixtures of GP experts, a stochastic process model which i) allows exact posterior inference, ii) has attractive computational and memory costs, and iii) when used as GP approximation, captures predictive uncertainties consistently better than previous expert-based approximations. In a variety of experiments, we show that deep structured mixtures have a low approximation error and often perform competitive or outperform prior work.

Statistical Models for Natural Sounds

Richard E. Turner, 2010. Gatsby Computational Neuroscience Unit, UCL,

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It is important to understand the rich structure of natural sounds in order to solve important tasks, like automatic speech recognition, and to understand auditory processing in the brain. This thesis takes a step in this direction by characterising the statistics of simple natural sounds. We focus on the statistics because perception often appears to depend on them, rather than on the raw waveform. For example the perception of auditory textures, like running water, wind, fire and rain, depends on summary-statistics, like the rate of falling rain droplets, rather than on the exact details of the physical source. In order to analyse the statistics of sounds accurately it is necessary to improve a number of traditional signal processing methods, including those for amplitude demodulation, time-frequency analysis, and sub-band demodulation. These estimation tasks are ill-posed and therefore it is natural to treat them as Bayesian inference problems. The new probabilistic versions of these methods have several advantages. For example, they perform more accurately on natural signals and are more robust to noise, they can also fill-in missing sections of data, and provide error-bars. Furthermore, free-parameters can be learned from the signal. Using these new algorithms we demonstrate that the energy, sparsity, modulation depth and modulation time-scale in each sub-band of a signal are critical statistics, together with the dependencies between the sub-band modulators. In order to validate this claim, a model containing co-modulated coloured noise carriers is shown to be capable of generating a range of realistic sounding auditory textures. Finally, we explored the connection between the statistics of natural sounds and perception. We demonstrate that inference in the model for auditory textures qualitatively replicates the primitive grouping rules that listeners use to understand simple acoustic scenes. This suggests that the auditory system is optimised for the statistics of natural sounds.

Gaussian Processes for State Space Models and Change Point Detection

Ryan Darby Turner, 2011. University of Cambridge, Department of Engineering, Cambridge, UK.

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This thesis details several applications of Gaussian processes (GPs) for enhanced time series modeling. We first cover different approaches for using Gaussian processes in time series problems. These are extended to the state space approach to time series in two different problems. We also combine Gaussian processes and Bayesian online change point detection (BOCPD) to increase the generality of the Gaussian process time series methods. These methodologies are evaluated on predictive performance on six real world data sets, which include three environmental data sets, one financial, one biological, and one from industrial well drilling. Gaussian processes are capable of generalizing standard linear time series models. We cover two approaches: the Gaussian process time series model (GPTS) and the autoregressive Gaussian process (ARGP). We cover a variety of methods that greatly reduce the computational and memory complexity of Gaussian process approaches, which are generally cubic in computational complexity. Two different improvements to state space based approaches are covered. First, Gaussian process inference and learning (GPIL) generalizes linear dynamical systems (LDS), for which the Kalman filter is based, to general nonlinear systems for nonparametric system identification. Second, we address pathologies in the unscented Kalman filter (UKF). We use Gaussian process optimization (GPO) to learn UKF settings that minimize the potential for sigma point collapse. We show how to embed mentioned Gaussian process approaches to time series into a change point framework. Old data, from an old regime, that hinders predictive performance is automatically and elegantly phased out. The computational improvements for Gaussian process time series approaches are of even greater use in the change point framework. We also present a supervised framework learning a change point model when change point labels are available in training. These mentioned methodologies significantly improve predictive performance on the diverse set of data sets selected.

System Identification in Gaussian Process Dynamical Systems

Ryan Turner, Marc Peter Deisenroth, Carl Edward Rasmussen, December 2009. (In NIPS Workshop on Nonparametric Bayes). Edited by Dilan Görür. Whistler, BC, Canada.

URL

Comment: poster.

State-Space Inference and Learning with Gaussian Processes

Ryan Turner, Marc Peter Deisenroth, Carl Edward Rasmussen, May 13–15 2010. (In 13th International Conference on Artificial Intelligence and Statistics). Edited by Yee Whye Teh, Mike Titterington. Chia Laguna, Sardinia, Italy. W & CP.

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State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model.

Comment: poster.

Model Based Learning of Sigma Points in Unscented Kalman Filtering

Ryan Turner, Carl Edward Rasmussen, August 2010. (In Machine Learning for Signal Processing (MLSP 2010)). Edited by Samuel Kaski, David J. Miller, Erkki Oja, Antti Honkela. Kittilä, Finland. ISBN: 978-1-4244-7876-7.

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The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for a step known as sigma point placement, causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L.

Model based learning of sigma points in unscented Kalman filtering

Ryan D. Turner, Carl Edward Rasmussen, 2012. (Neurocomputing). DOI: 10.1016/j.neucom.2011.07.029.

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The unscented Kalman filter (UKF) is a widely used method in control and time series applications. The UKF suffers from arbitrary parameters necessary for sigma point placement, potentially causing it to perform poorly in nonlinear problems. We show how to treat sigma point placement in a UKF as a learning problem in a model based view. We demonstrate that learning to place the sigma points correctly from data can make sigma point collapse much less likely. Learning can result in a significant increase in predictive performance over default settings of the parameters in the UKF and other filters designed to avoid the problems of the UKF, such as the GP-ADF. At the same time, we maintain a lower computational complexity than the other methods. We call our method UKF-L.

Demodulation as Probabilistic Inference

Richard E. Turner, Maneesh Sahani, 2011. (Transactions on Audio, Speech and Language Processing).

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Demodulation is an ill-posed problem whenever both carrier and envelope signals are broadband and unknown. Here, we approach this problem using the methods of probabilistic inference. The new approach, called Probabilistic Amplitude Demodulation (PAD), is computationally challenging but improves on existing methods in a number of ways. By contrast to previous approaches to demodulation, it satisfies five key desiderata: PAD has soft constraints because it is probabilistic; PAD is able to automatically adjust to the signal because it learns parameters; PAD is user-steerable because the solution can be shaped by user-specific prior information; PAD is robust to broad-band noise because this is modelled explicitly; and PAD’s solution is self-consistent, empirically satisfying a Carrier Identity property. Furthermore, the probabilistic view naturally encompasses noise and uncertainty, allowing PAD to cope with missing data and return error bars on carrier and envelope estimates. Finally, we show that when PAD is applied to a bandpass-filtered signal, the stop-band energy of the inferred carrier is minimal, making PAD well-suited to sub-band demodulation.

Probabilistic amplitude and frequency demodulation

Richard E. Turner, Maneesh Sahani, 2011. (In Advances in Neural Information Processing Systems 24). The MIT Press.

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A number of recent scientific and engineering problems require signals to be decomposed into a product of a slowly varying positive envelope and a quickly varying carrier whose instantaneous frequency also varies slowly over time. Although signal processing provides algorithms for so-called amplitude- and frequency-demodulation (AFD), there are well known problems with all of the existing methods. Motivated by the fact that AFD is ill-posed, we approach the problem using probabilistic inference. The new approach, called probabilistic amplitude and frequency demodulation (PAFD), models instantaneous frequency using an auto-regressive generalization of the von Mises distribution, and the envelopes using Gaussian auto-regressive dynamics with a positivity constraint. A novel form of expectation propagation is used for inference. We demonstrate that although PAFD is computationally demanding, it outperforms previous approaches on synthetic and real signals in clean, noisy and missing data settings.

Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian Processes

Andrew Gordon Wilson, 2014. University of Cambridge, Cambridge, UK.

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Truly intelligent systems are capable of pattern discovery and extrapolation without human intervention. Bayesian nonparametric models, which can uniquely represent expressive prior information and detailed inductive biases, provide a distinct opportunity to develop intelligent systems, with applications in essentially any learning and prediction task. Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation. A covariance kernel determines the support and inductive biases of a Gaussian process. In this thesis, we introduce new covariance kernels to enable fast automatic pattern discovery and extrapolation with Gaussian processes. In the introductory chapter, we discuss the high level principles behind all of the models in this thesis: 1) we can typically improve the predictive performance of a model by accounting for additional structure in data; 2) to automatically discover rich structure in data, a model must have large support and the appropriate inductive biases; 3) we most need expressive models for large datasets, which typically provide more information for learning structure, and 4) we can often exploit the existing inductive biases (assumptions) or structure of a model for scalable inference, without the need for simplifying assumptions. In the context of this introduction, we then discuss, in chapter 2, Gaussian processes as kernel machines, and my views on the future of Gaussian process research. In chapter 3 we introduce the Gaussian process regression network (GPRN) framework, a multi-output Gaussian process method which scales to many output variables, and accounts for input-dependent correlations between the outputs. Underlying the GPRN is a highly expressive kernel, formed using an adaptive mixture of latent basis functions in a neural network like architecture. The GPRN is capable of discovering expressive structure in data. We use the GPRN to model the time-varying expression levels of 1000 genes, the spatially varying concentrations of several distinct heavy metals, and multivariate volatility (input dependent noise covariances) between returns on equity indices and currency exchanges, which is particularly valuable for portfolio allocation. We generalise the GPRN to an adaptive network framework, which does not depend on Gaussian processes or Bayesian nonparametrics; and we outline applications for the adaptive network in nuclear magnetic resonance (NMR) spectroscopy, ensemble learning, and change-point modelling. In chapter 4 we introduce simple closed form kernel for automatic pattern discovery and extrapolation. These spectral mixture (SM) kernels are derived by modelling the spectral densiy of a kernel (its Fourier transform) using a scale-location Gaussian mixture. SM kernels form a basis for all stationary covariances, and can be used as a drop-in replacement for standard kernels, as they retain simple and exact learning and inference procedures. We use the SM kernel to discover patterns and perform long range extrapolation on atmospheric CO2 trends and airline passenger data, as well as on synthetic examples. We also show that the SM kernel can be used to automatically reconstruct several standard covariances. The SM kernel and the GPRN are highly complementary; we show that using the SM kernel with adaptive basis functions in a GPRN induces an expressive prior over non-stationary kernels. In chapter 5 we introduce GPatt, a method for fast multidimensional pattern extrapolation, particularly suited to imge and movie data. Without human intervention – no hand crafting of kernel features, and no sophisticated initialisation procedures – we show that GPatt can solve large scale pattern extrapolation, inpainting and kernel discovery problems, including a problem with 383,400 training points. GPatt exploits the structure of a spectral mixture product (SMP) kernel, for fast yet exact inference procedures. We find that GPatt significantly outperforms popular alternative scalable gaussian process methods in speed and accuracy. Moreover, we discover profound differences between each of these methods, suggesting expressive kernels, nonparametric representations, and scalable inference which exploits existing model structure are useful in combination for modelling large scale multidimensional patterns. The models in this dissertation have proven to be scalable and with greatly enhanced predictive performance over the alternatives: the extra structure being modelled is an important part of a wide variety of real data – including problems in econometrics, gene expression, geostatistics, nuclear magnetic resonance spectroscopy, ensemble learning, multi-output regression, change point modelling, time series, multivariate volatility, image inpainting, texture extrapolation, video extrapolation, acoustic modelling, and kernel discovery.

Gaussian Process Kernels for Pattern Discovery and Extrapolation

Andrew Gordon Wilson, Ryan Prescott Adams, February 18 2013. (In 30th International Conference on Machine Learning).

Abstract▼ URL

Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation. We introduce simple closed form kernels that can be used with Gaussian processes to discover patterns and enable extrapolation. These kernels are derived by modelling a spectral density – the Fourier transform of a kernel – with a Gaussian mixture. The proposed kernels support a broad class of stationary covariances, but Gaussian process inference remains simple and analytic. We demonstrate the proposed kernels by discovering patterns and performing long range extrapolation on synthetic examples, as well as atmospheric CO2 trends and airline passenger data. We also show that we can reconstruct standard covariances within our framework.

Comment: arXiv:1302.4245

Copula Processes

Andrew Gordon Wilson, Zoubin Ghahramani, 2010. (In Advances in Neural Information Processing Systems 23). Note: Spotlight.

Abstract▼ URL

We define a copula process which describes the dependencies between arbitrarily many random variables independently of their marginal distributions. As an example, we develop a stochastic volatility model, Gaussian Copula Process Volatility (GCPV), to predict the latent standard deviations of a sequence of random variables. To make predictions we use Bayesian inference, with the Laplace approximation, and with Markov chain Monte Carlo as an alternative. We find our model can outperform GARCH on simulated and financial data. And unlike GARCH, GCPV can easily handle missing data, incorporate covariates other than time, and model a rich class of covariance structures.

Comment: Supplementary Material, slides.

Generalised Wishart Processes

Andrew Gordon Wilson, Zoubin Ghahramani, 2011. (In 27th Conference on Uncertainty in Artificial Intelligence).

Abstract▼ URL

We introduce a new stochastic process called the generalised Wishart process (GWP). It is a collection of positive semi-definite random matrices indexed by any arbitrary input variable. We use this process as a prior over dynamic (e.g. time varying) covariance matrices. The GWP captures a diverse class of covariance dynamics, naturally hanles missing data, scales nicely with dimension, has easily interpretable parameters, and can use input variables that include covariates other than time. We describe how to construct the GWP, introduce general procedures for inference and prediction, and show that it outperforms its main competitor, multivariate GARCH, even on financial data that especially suits GARCH.

Comment: Supplementary Material, Best Student Paper Award

Modelling Input Varying Correlations between Multiple Responses

Andrew Gordon Wilson, Zoubin Ghahramani, 2012. (In ECML/PKDD). Edited by Peter A. Flach, Tijl De Bie, Nello Cristianini. Springer. Lecture Notes in Computer Science. ISBN: 978-3-642-33485-6.

Abstract▼ URL

We introduced a generalised Wishart process (GWP) for modelling input dependent covariance matrices Σ(x), allowing one to model input varying correlations and uncertainties between multiple response variables. The GWP can naturally scale to thousands of response variables, as opposed to competing multivariate volatility models which are typically intractable for greater than 5 response variables. The GWP can also naturally capture a rich class of covariance dynamics – periodicity, Brownian motion, smoothness, …– through a covariance kernel.

GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes

Andrew Gordon Wilson, Elad Gilboa, Arye Nehorai, John P Cunningham, 2013. (arXiv preprint arXiv:1310.5288).

Abstract▼ URL

Gaussian processes are typically used for smoothing and interpolation on small datasets. We introduce a new Bayesian nonparametric framework – GPatt – enabling automatic pattern extrapolation with Gaussian processes on large multidimensional datasets. GPatt unifies and extends highly expressive kernels and fast exact inference techniques. Without human intervention – no hand crafting of kernel features, and no sophisticated initialisation procedures – we show that GPatt can solve large scale pattern extrapolation, inpainting, and kernel discovery problems, including a problem with 383,400 training points. We find that GPatt significantly outperforms popular alternative scalable Gaussian process methods in speed and accuracy. Moreover, we discover profound differences between each of these methods, suggesting expressive kernels, nonparametric representations, and scalable inference which exploits model structure are useful in combination for modelling large scale multidimensional patterns.

Gaussian Process Regression Networks

Andrew Gordon Wilson, David A Knowles, Zoubin Ghahramani, October 19 2011. Department of Engineering, University of Cambridge, Cambridge, UK.

Abstract▼ URL

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.

Comment: arXiv:1110.4411

Gaussian Process Regression Networks

Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani, June 2012. (In 29th International Conference on Machine Learning). Edinburgh, Scotland.

Abstract▼ URL

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the nonparametric flexibility of Gaussian processes. GPRN accommodates input (predictor) dependent signal and noise correlations between multiple output (response) variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both elliptical slice sampling and variational Bayes inference procedures for GPRN. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on real datasets, including a 1000 dimensional gene expression dataset.

Gaussian processes for regression

Chris K. I. Williams, Carl Edward Rasmussen, 1996. (In Advances in Neural Information Processing Systems 8). Edited by D. S. Touretzky, M. C. Mozer, M. E. Hasselmo. Cambridge, MA., USA. The MIT Press.

Abstract▼ URL

The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior over functions. We investigate the use of a Gaussian process prior over functions, which permits the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations. Two methods, using optimization and averaging (via Hybrid Monte Carlo) over hyperparameters have been tested on a number of challenging problems and have produced excellent results.

Convolutional Gaussian Processes

Mark van der Wilk, Carl Edward Rasmussen, James Hensman, 2017. (In Advances in Neural Information Processing Systems 31).

Abstract▼ URL

We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images. The main contribution of our work is the construction of an inter-domain inducing point approximation that is well-tailored to the convolutional kernel. This allows us to gain the generalisation benefit of a convolutional kernel, together with fast but accurate posterior inference. We investigate several variations of the convolutional kernel, and apply it to MNIST and CIFAR-10, which have both been known to be challenging for Gaussian processes. We also show how the marginal likelihood can be used to find an optimal weighting between convolutional and RBF kernels to further improve performance. We hope that this illustration of the usefulness of a marginal likelihood will help automate discovering architectures in larger models.

Comment: arXiv

Observations on the Nyström Method for Gaussian Process Prediction

Christopher K. I. Williams, Carl Edward Rasmussen, Anton Schwaighofer, Volker Tresp, 2002. University of Edinburgh,

Abstract▼ URL

A number of methods for speeding up Gaussian Process (GP) prediction have been proposed, including the Nyström method of Williams and Seeger (2001). In this paper we focus on two issues (1) the relationship of the Nyström method to the Subset of Regressors method (Poggio and Girosi 1990; Luo and Wahba, 1997) and (2) understanding in what circumstances the Nyström approximation would be expected to provide a good approximation to exact GP regression.

Bayesian Inference for NMR Spectroscopy with Applications to Chemical Quantification

Andrew Gordon Wilson, Yuting Wu, Daniel J. Holland, Sebastian Nowozin, Mick D. Mantle, Lynn F. Gladden, Andrew Blake, 2014. (arXiv preprint arXiv 1402.3580).

Abstract▼ URL

Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic properties of atomic nuclei to discover the structure, reaction state and chemical environment of molecules. We propose a probabilistic generative model and inference procedures for NMR spectroscopy. Specifically, we use a weighted sum of trigonometric functions undergoing exponential decay to model free induction decay (FID) signals. We discuss the challenges in estimating the components of this general model – amplitudes, phase shifts, frequencies, decay rates, and noise variances – and offer practical solutions. We compare with conventional Fourier transform spectroscopy for estimating the relative concentrations of chemicals in a mixture, using synthetic and experimentally acquired FID signals. We find the proposed model is particularly robust to low signal to noise ratios (SNR), and overlapping peaks in the Fourier transform of the FID, enabling accurate predictions (e.g., 1% error at low SNR) which are not possible with conventional spectroscopy (5% error).

Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity

B. M. Yu, J. P. Cunningham, G. Santhanam, S. I. Ryu, K. V. Shenoy, M. Sahani, 2009. (Journal of Neurophysiology).

Abstract▼ URL

We consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from many neurons on individual experimental trials. Beyond the benefit of visualizing the high-dimensional, noisy spiking activity in a compact form, such trajectories can offer insight into the dynamics of the neural circuitry underlying the recorded activity. Current methods for extracting neural trajectories involve a two-stage process: the spike trains are first smoothed over time, then a static dimensionality- reduction technique is applied. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way and that account for spiking variability, which may vary both across neurons and across time. We then present a novel method for extracting neural trajectories – Gaussian-process factor analysis (GPFA) – which unifies the smoothing and dimensionality- reduction operations in a common probabilistic framework. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that the proposed extensions improved the predictive ability of the two-stage methods. The predictive ability was further improved by going to GPFA. From the extracted trajectories, we directly observed a convergence in neural state during motor planning, an effect that was shown indirectly by previous studies. We then show how such methods can be a powerful tool for relating the spiking activity across a neural population to the subject’s behavior on a single-trial basis. Finally, to assess how well the proposed methods characterize neural population activity when the underlying time course is known, we performed simulations that revealed that GPFA performed tens of percent better than the best two-stage method.

Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity

B. M. Yu, J. P. Cunningham, G. Santhanam, S. I. Ryu, K. V. Shenoy, M. Sahani, December 2009. (In Advances in Neural Information Processing Systems 21). Vancouver, BC.

Abstract▼ URL

We consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from many neurons on individual experimental trials. Beyond the benefit of visualizing the high-dimensional, noisy spiking activity in a compact form, such trajectories can offer insight into the dynamics of the neural circuitry underlying the recorded activity. Current methods for extracting neural trajectories involve a two-stage process: the spike trains are first smoothed over time, then a static dimensionality- reduction technique is applied. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way and that account for spiking variability, which may vary both across neurons and across time. We then present a novel method for extracting neural trajectories – Gaussian-process factor analysis (GPFA) – which unifies the smoothing and dimensionality- reduction operations in a common probabilistic framework. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that the proposed extensions improved the predictive ability of the two-stage methods. The predictive ability was further improved by going to GPFA. From the extracted trajectories, we directly observed a convergence in neural state during motor planning, an effect that was shown indirectly by previous studies. We then show how such methods can be a powerful tool for relating the spiking activity across a neural population to the subject’s behavior on a single-trial basis. Finally, to assess how well the proposed methods characterize neural population activity when the underlying time course is known, we performed simulations that revealed that GPFA performed tens of percent better than the best two-stage method.

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