Search Results for author: Peter Mostowsky

Found 4 papers, 3 papers with code

Pathwise Conditioning of Gaussian Processes

2 code implementations8 Nov 2020 James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth

As Gaussian processes are used to answer increasingly complex questions, analytic solutions become scarcer and scarcer.

Gaussian Processes

Matérn Gaussian Processes on Graphs

no code implementations29 Oct 2020 Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, Nicolas Durrande

Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information about their properties.

Gaussian Processes

Matérn Gaussian processes on Riemannian manifolds

1 code implementation NeurIPS 2020 Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth

Gaussian processes are an effective model class for learning unknown functions, particularly in settings where accurately representing predictive uncertainty is of key importance.

Gaussian Processes

Efficiently Sampling Functions from Gaussian Process Posteriors

5 code implementations ICML 2020 James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth

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.

Gaussian Processes

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