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Found 6 papers, 4 papers with code
Memory-Based Dual Gaussian Processes for Sequential Learning
Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning.
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning.
Dual Parameterization of Sparse Variational Gaussian Processes
Sparse variational Gaussian process (SVGP) methods are a common choice for non-conjugate Gaussian process inference because of their computational benefits.
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
EP provides some benefits over the traditional methods via introduction of the so-called cavity distribution, and we combine these benefits with the computational efficiency of linearisation, providing extensive empirical analysis demonstrating the efficacy of various algorithms under this unifying framework.
Fast Variational Learning in State-Space Gaussian Process Models
Gaussian process (GP) regression with 1D inputs can often be performed in linear time via a stochastic differential equation formulation.
Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes
The extended Kalman filter (EKF) is a classical signal processing algorithm which performs efficient approximate Bayesian inference in non-conjugate models by linearising the local measurement function, avoiding the need to compute intractable integrals when calculating the posterior.
