Selected Publications

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Arno SolinJames HensmanRichard E. Turner
Infinite-horizon Gaussian processes
Advances in Neural Information Processing Systems (NeurIPS) 2018
Abstract: Gaussian 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...

Simo SärkkäArno Solin
Applied Stochastic Differential Equations
Cambridge University Press 2019
Abstract: Stochastic differential equations are differential equations whose solutions are stochastic processes. They exhibit appealing mathematical properties that are useful in modeling uncertainties and noisy phenomena in many disciplines. This book is motivated by applications of...

Arno SolinSimo Särkkä
Hilbert Space Methods for Reduced-Rank Gaussian Process Regression
Statistics and Computing 2020
Abstract: This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based on an approximate series expansion of the covariance function in terms of an eigenfunction expansion of the Laplace operator in...

Lassi MeronenChristabella IrwantoArno Solin
Stationary Activations for Uncertainty Calibration in Deep Learning
Advances in Neural Information Processing Systems (NeurIPS) 2020
Abstract: 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) models. This class spans a range of locally...

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