2 code implementations • 10 Jul 2024 • Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov, Noémie Jaquier, Michael John Hutchinson, Aditya Ravuri, Leonel Rozo, Alexander Terenin, Viacheslav Borovitskiy

To address this difficulty, we present GeometricKernels, a software package which implements the geometric analogs of classical Euclidean squared exponential - also known as heat - and Mat\'ern kernels, which are widely-used in settings where uncertainty is of key interest.

1 code implementation • NeurIPS 2023 • Bernardo Fichera, Viacheslav Borovitskiy, Andreas Krause, Aude Billard

Gaussian process regression is widely used because of its ability to provide well-calibrated uncertainty estimates and handle small or sparse datasets.

1 code implementation • 30 Oct 2023 • Maosheng Yang, Viacheslav Borovitskiy, Elvin Isufi

We propose principled Gaussian processes (GPs) for modeling functions defined over the edge set of a simplicial 2-complex, a structure similar to a graph in which edges may form triangular faces.

1 code implementation • 28 Oct 2023 • Daniel Robert-Nicoud, Andreas Krause, Viacheslav Borovitskiy

Various applications ranging from robotics to climate science require modeling signals on non-Euclidean domains, such as the sphere.

1 code implementation • NeurIPS 2023 • Paul Rosa, Viacheslav Borovitskiy, Alexander Terenin, Judith Rousseau

Gaussian processes are used in many machine learning applications that rely on uncertainty quantification.

1 code implementation • 30 Jan 2023 • Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy

The invariance of a Gaussian process' covariance to such symmetries gives rise to the most natural generalization of the concept of stationarity to such spaces.

no code implementations • 10 Nov 2022 • Iskander Azangulov, Viacheslav Borovitskiy, Andrei Smolensky

In this note, we introduce a family of "power sum" kernels and the corresponding Gaussian processes on symmetric groups $\mathrm{S}_n$.

3 code implementations • 3 Nov 2022 • Viacheslav Borovitskiy, Mohammad Reza Karimi, Vignesh Ram Somnath, Andreas Krause

We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops.

no code implementations • 4 Oct 2022 • Noémie Jaquier, Leonel Rozo, Miguel González-Duque, Viacheslav Borovitskiy, Tamim Asfour

This may be attributed to the lack of computational models that fill the gap between the discrete hierarchical structure of the taxonomy and the high-dimensional heterogeneous data associated to its categories.

1 code implementation • 31 Aug 2022 • Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy

The invariance of a Gaussian process' covariance to such symmetries gives rise to the most natural generalization of the concept of stationarity to such spaces.

1 code implementation • 2 Nov 2021 • Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky, Alexander Terenin, Tamim Asfour, Leonel Rozo

Bayesian optimization is a data-efficient technique which can be used for control parameter tuning, parametric policy adaptation, and structure design in robotics.

no code implementations • NeurIPS 2021 • Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth

Gaussian processes are machine learning models capable of learning unknown functions in a way that represents uncertainty, thereby facilitating construction of optimal decision-making systems.

1 code implementation • 11 Feb 2021 • Fedor Pavutnitskiy, Sergei O. Ivanov, Evgeny Abramov, Viacheslav Borovitskiy, Artem Klochkov, Viktor Vialov, Anatolii Zaikovskii, Aleksandr Petiushko

The knowledge that data lies close to a particular submanifold of the ambient Euclidean space may be useful in a number of ways.

2 code implementations • 8 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.

no code implementations • 29 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.

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.

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.

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