667 citations · 1.5k across the 26 of their papers we have counts for
24 papers · 1 filter
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes
Talay M Cheema, Carl Edward Rasmussen
Sparse variational approximations are popular methods for scaling up inference and learning in Gaussian processes to larger datasets. For training points, exact inference has $…
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt +1
The kernel function and its hyperparameters are the central model selection choice in a Gaussian proces (Rasmussen and Williams, 2006). Typically, the hyperparameters of the kernel…
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
Alexander Terenin, David R. Burt, Artem Artemev +4
Gaussian processes are frequently deployed as part of larger machine learning and decision-making systems, for instance in geospatial modeling, Bayesian optimization, or in latent…
Kernel Identification Through Transformers
Fergus Simpson, Ian Davies, Vidhi Lalchand +3
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…
The Promises and Pitfalls of Deep Kernel Learning
Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk
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 cr…
Marginalised Gaussian Processes with Nested Sampling
Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
Gaussian Process (GPs) models are a rich distribution over functions with inductive biases controlled by a kernel function. Learning occurs through the optimisation of kernel hyper…
Convergence of Sparse Variational Inference in Gaussian Processes Regression
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
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…
Variational Orthogonal Features
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Sparse stochastic variational inference allows Gaussian process models to be applied to large datasets. The per iteration computational cost of inference with this method is $\math…
Benchmarking the Neural Linear Model for Regression
Sebastian W. Ober, Carl Edward Rasmussen
The neural linear model is a simple adaptive Bayesian linear regression method that has recently been used in a number of problems ranging from Bayesian optimization to reinforceme…
Approximate Inference for Fully Bayesian Gaussian Process Regression
Vidhi Lalchand, Carl Edward Rasmussen
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…
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman +1
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 comp…
Rates of Convergence for Sparse Variational Gaussian Process Regression
David R. Burt, Carl E. Rasmussen, Mark van der Wilk
Excellent variational approximations to Gaussian process posteriors have been developed which avoid the scaling with dataset size . They reduce the…
Non-Factorised Variational Inference in Dynamical Systems
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman +1
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 f…
Closed-form Inference and Prediction in Gaussian Process State-Space Models
Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen
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.…
Convolutional Gaussian Processes
Mark van der Wilk, Carl Edward Rasmussen, James Hensman
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…
Understanding Probabilistic Sparse Gaussian Process Approximations
Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen
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 Indep…
Data-Efficient Reinforcement Learning in Continuous-State POMDPs
Rowan McAllister, Carl Edward Rasmussen
We present a data-efficient reinforcement learning algorithm resistant to observation noise. Our method extends the highly data-efficient PILCO algorithm (Deisenroth & Rasmussen, 2…
Gaussian Processes for Data-Efficient Learning in Robotics and Control
Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen
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, w…
Manifold Gaussian Processes for Regression
Roberto Calandra, Jan Peters, Carl Edward Rasmussen +1
Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness assumptions on the structure of the function to be modeled. To model complex and non-differentiable funct…
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
Yarin Gal, Mark van der Wilk, Carl E. Rasmussen
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 offe…
Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM
Roger Frigola, Fredrik Lindsten, Thomas B. Schön +1
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 dyna…
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
Roger Frigola, Fredrik Lindsten, Thomas B. Schön +1
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 inf…
Additive Gaussian Processes
David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen
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…
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Hannes Nickisch, Carl Edward Rasmussen
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 characteristi…