38 citations · 97 across the 29 of their papers we have counts for
6 papers · 2 filters
Spatio-Temporal Variational Gaussian Processes
Oliver Hamelijnck, William J. Wilkinson, Niki A. Loppi +2
We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filtering with natural gradient variational inference, resulting in a non-conjugate GP…
Non-separable Spatio-temporal Graph Kernels via SPDEs
Alexander Nikitin, ST John, Arno Solin +1
Gaussian processes (GPs) provide a principled and direct approach for inference and learning on graphs. However, the lack of justified graph kernels for spatio-temporal modelling h…
Dual Parameterization of Sparse Variational Gaussian Processes
Vincent Adam, Paul E. Chang, Mohammad Emtiyaz Khan +1
Sparse variational Gaussian process (SVGP) methods are a common choice for non-conjugate Gaussian process inference because of their computational benefits. In this paper, we impro…
Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation
Arno Solin, Ella Tamir, Prakhar Verma
Simulation-based techniques such as variants of stochastic Runge-Kutta are the de facto approach for inference with stochastic differential equations (SDEs) in machine learning. Th…
Periodic Activation Functions Induce Stationarity
Lassi Meronen, Martin Trapp, Arno Solin
Neural network models are known to reinforce hidden data biases, making them unreliable and difficult to interpret. We seek to build models that `know what they do not know' by int…
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
Will Tebbutt, Arno Solin, Richard E. Turner
Gaussian processes (GPs) are important probabilistic tools for inference and learning in spatio-temporal modelling problems such as those in climate science and epidemiology. Howev…