Add to ORKGGaussian processes provide a flexible framework for forecasting, removing noise, and interpreting long temporal datasets. State space modelling (Kalman filtering) enables these non-parametric models to be deployed on long datasets by reducing the complexity to linear in the number of data points. The complexity is still cubic in the state dimension m which is an impediment to practical application. In certain special cases (Gaussian likelihood, regular spacing) the GP posterior will reach a steady posterior state when the data are very long. We leverage this and formulate an inference scheme for GPs with general likelihoods, where inference is based on single-sweep EP (assumed density filtering). The infinite-horizon model tackles the cubic...
We develop an approach for a sparse representation for Gaussian Process (GP) models in order to over...
Exact Gaussian process (GP) regression is not available for n 10, 000 (O(n3) for learning and O(n) ...
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to in...
Gaussian processes provide a flexible framework for forecasting, removing noise, and interpreting lo...
The analysis of time series data is important for many fields, ranging from meteorology and engineer...
Abstract—Gaussian process based machine learning is a power-ful Bayesian paradigm for non-parametric...
Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temp...
Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while c...
Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while c...
The analysis of time series data is important in fields as disparate as the social sciences, biology...
Abstract. The expressive power of Gaussian process (GP) models comes at a cost of poor scalability i...
Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temp...
The expressive power of Gaussian process (GP) models comes at a cost of poor scalability in the size...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
We explore ways to scale Gaussian processes (GP) to large datasets. Two methods with different theor...
We develop an approach for a sparse representation for Gaussian Process (GP) models in order to over...
Exact Gaussian process (GP) regression is not available for n 10, 000 (O(n3) for learning and O(n) ...
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to in...
Gaussian processes provide a flexible framework for forecasting, removing noise, and interpreting lo...
The analysis of time series data is important for many fields, ranging from meteorology and engineer...
Abstract—Gaussian process based machine learning is a power-ful Bayesian paradigm for non-parametric...
Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temp...
Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while c...
Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while c...
The analysis of time series data is important in fields as disparate as the social sciences, biology...
Abstract. The expressive power of Gaussian process (GP) models comes at a cost of poor scalability i...
Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temp...
The expressive power of Gaussian process (GP) models comes at a cost of poor scalability in the size...
State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose ...
We explore ways to scale Gaussian processes (GP) to large datasets. Two methods with different theor...
We develop an approach for a sparse representation for Gaussian Process (GP) models in order to over...
Exact Gaussian process (GP) regression is not available for n 10, 000 (O(n3) for learning and O(n) ...
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to in...