1 code implementation • 6 Jun 2023 • Paul E. Chang, Prakhar Verma, S. T. John, Arno Solin, Mohammad Emtiyaz Khan
Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning.
no code implementations • 2 Nov 2022 • Paul E. Chang, Prakhar Verma, ST John, Victor Picheny, Henry Moss, Arno Solin
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning.
1 code implementation • NeurIPS 2021 • Vincent Adam, Paul E. Chang, Mohammad Emtiyaz Khan, Arno Solin
Sparse variational Gaussian process (SVGP) methods are a common choice for non-conjugate Gaussian process inference because of their computational benefits.
1 code implementation • ICML 2020 • William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin
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.
1 code implementation • 9 Jul 2020 • Paul E. Chang, William J. Wilkinson, Mohammad Emtiyaz Khan, Arno Solin
Gaussian process (GP) regression with 1D inputs can often be performed in linear time via a stochastic differential equation formulation.
no code implementations • pproximateinference AABI Symposium 2019 • William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin
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.