Peter Mostowsky — accepted AI-conference papers

2021

Matérn Gaussian Processes on Graphs

Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Deisenroth, Nicolas Durrande

AISTATS 2021poster

Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information about their properties. Although many different Gaussian process models are readily available when the input space is Euclidean, the choice is much more limited for G…

Cited by 113 Source ↗PDF ↗Scholar ↗

2020

Efficiently sampling functions from Gaussian process posteriors

James Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth

ICML 2020poster

Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model’s success hinges upon its ability to faithfully represent predictive uncertainty. These problems typically exist as parts of larger frameworks, wherein quantities of interest are ultimat…

Cited by 186 Source ↗PDF ↗code ↗Scholar ↗

2020

Matérn Gaussian Processes on Riemannian Manifolds

Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth (he/him

NeurIPS 2020poster

Gaussian processes are an effective model class for learning unknown functions, particularly in settings where accurately representing predictive uncertainty is of key importance. Motivated by applications in the physical sciences, the widely-used Matérn class of Gaussian processes has recently been…

Cited by 158 Source ↗PDF ↗code ↗Scholar ↗