99 citations · 347 across the 39 of their papers we have counts for
6 papers · 2 filters
Harmonizable mixture kernels with variational Fourier features
Zheyang Shen, Markus Heinonen, Samuel Kaski
The expressive power of Gaussian processes depends heavily on the choice of kernel. In this work we propose the novel harmonizable mixture kernel (HMK), a family of expressive, int…
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching
Cagatay Yildiz, Markus Heinonen, Jukka Intosalmi +2
We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE). The proposed model learns to simulate path distr…
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
Markus Heinonen, Maria Osmala, Henrik Mannerström +4
Metabolic flux balance analyses are a standard tool in analysing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometr…
mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion
Emmi Jokinen, Markus Heinonen, Harri Lähdesmäki
Proteins are commonly used by biochemical industry for numerous processes. Refining these proteins' properties via mutations causes stability effects as well. Accurate computationa…
Variational zero-inflated Gaussian processes with sparse kernels
Pashupati Hegde, Markus Heinonen, Samuel Kaski
Zero-inflated datasets, which have an excess of zero outputs, are commonly encountered in problems such as climate or rare event modelling. Conventional machine learning approaches…
Learning unknown ODE models with Gaussian processes
Markus Heinonen, Cagatay Yildiz, Henrik Mannerström +2
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…