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Henrik Mannerström

2 accepted papers

2018

Learning unknown ODE models with Gaussian processes

ICML 2018oral

In conventional ODE modelling coefficients of an equation driving the system state forward in time are estimated. However, for many complex systems it is practically impossible to determine the equations or interactions governing the underlying dynamics. In these settings, parametric ODE model canno…

2016

Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo

AISTATS 2016poster

We present a novel approach for non-stationary Gaussian process regression (GPR), where the three key parameters – noise variance, signal variance and lengthscale – can be simultaneously input-dependent. We develop gradient-based inference methods to learn the unknown function and the non-stationary…