Viacheslav Borovitskiy

According to our database, Viacheslav Borovitskiy authored at least 23 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks?

Pedro C. Vieira, Pedro Ribeiro, Viacheslav Borovitskiy

CoRR, May, 2026

Heat and Matérn Kernels on Matchings.

Dmitry Eremeev, Salem Said, Viacheslav Borovitskiy

CoRR, April, 2026

Bayesian Scattering: A Principled Baseline for Uncertainty on Image Data.

Bernardo Fichera, Zarko Ivkovic, Kjell Jorner, Philipp Hennig, Viacheslav Borovitskiy

CoRR, March, 2026

2025

The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs.

Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov, Noémie Jaquier, Michael John Hutchinson, Aditya Ravuri, Leonel Rozo, Alexander Terenin, Viacheslav Borovitskiy

J. Mach. Learn. Res., 2025

Omnipresent Yet Overlooked: Heat Kernels in Combinatorial Bayesian Optimization.

Colin Doumont, Victor Picheny, Viacheslav Borovitskiy, Henry B. Moss

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Residual Deep Gaussian Processes on Manifolds.

Kacper Wyrwal, Andreas Krause, Viacheslav Borovitskiy

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces.

Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy

J. Mach. Learn. Res., 2024

Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case.

Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy

J. Mach. Learn. Res., 2024

Bringing Motion Taxonomies to Continuous Domains via GPLVM on Hyperbolic manifolds.

Noémie Jaquier, Leonel Rozo, Miguel González Duque, Viacheslav Borovitskiy, Tamim Asfour

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Hodge-Compositional Edge Gaussian Processes.

Maosheng Yang, Viacheslav Borovitskiy, Elvin Isufi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Intrinsic Gaussian Vector Fields on Manifolds.

Daniel Robert-Nicoud, Andreas Krause, Viacheslav Borovitskiy

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds.

Paul Rosa, Slava Borovitskiy, Alexander Terenin, Judith Rousseau

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Implicit Manifold Gaussian Process Regression.

Bernardo Fichera, Slava Borovitskiy, Andreas Krause, Aude Gemma Billard

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Isotropic Gaussian Processes on Finite Spaces of Graphs.

Viacheslav Borovitskiy, Mohammad Reza Karimi, Vignesh Ram Somnath, Andreas Krause

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Bringing robotics taxonomies to continuous domains via GPLVM on hyperbolic manifolds.

Noémie Jaquier, Leonel Dario Rozo, Miguel González Duque, Viacheslav Borovitskiy, Tamim Asfour

CoRR, 2022

Quadric Hypersurface Intersection for Manifold Learning in Feature Space.

Fedor Pavutnitskiy, Sergei O. Ivanov, Evgeniy Abramov, Viacheslav Borovitskiy, Artem Klochkov, Viktor Vyalov, Anatolii Zaikovskii, Aleksandr Petiushko

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Pathwise Conditioning of Gaussian Processes.

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

J. Mach. Learn. Res., 2021

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels.

Michael J. Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth

CoRR, 2021

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels.

Michael J. Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels.

Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky, Alexander Terenin, Tamim Asfour, Leonel Dario Rozo

Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021

Matérn Gaussian Processes on Graphs.

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

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Matérn Gaussian Processes on Riemannian Manifolds.

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficiently sampling functions from Gaussian process posteriors.

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

Proceedings of the 37th International Conference on Machine Learning, 2020