38 citations · 58 across the 13 of their papers we have counts for
11 papers · 1 filter
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds
Rui Li, ST John, Arno Solin
Gaussian process training decomposes into inference of the (approximate) posterior and learning of the hyperparameters. For non-Gaussian (non-conjugate) likelihoods, two common cho…
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Paul E. Chang, Prakhar Verma, ST John +3
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning. Their drawbacks are poor scaling with dat…
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…
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…
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…
Stationary Activations for Uncertainty Calibration in Deep Learning
Lassi Meronen, Christabella Irwanto, Arno Solin
We introduce a new family of non-linear neural network activation functions that mimic the properties induced by the widely-used Matérn family of kernels in Gaussian process (GP) m…