Alexander Terenin

According to our database, Alexander Terenin authored at least 30 papers between 2016 and 2024.

Collaborative distances:

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

2024

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees.

Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge

J. Mach. Learn. Res., 2024

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

CoRR, 2024

Cost-aware Bayesian optimization via the Pandora's Box Gittins index.

Qian Xie, Raul Astudillo, Peter I. Frazier, Ziv Scully, Alexander Terenin

CoRR, 2024

Stochastic Gradient Descent for Gaussian Processes Done Right.

Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz

Proceedings of the Twelfth International Conference on Learning Representations, 2024

A Unifying Variational Framework for Gaussian Process Motion Planning.

Lucas Cosier, Rares Iordan, Sicelukwanda N. T. Zwane, Giovanni Franzese, James T. Wilson, Marc Peter Deisenroth, Alexander Terenin, Yasemin Bekiroglu

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

2023

Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds.

Paul Rosa, Viacheslav Borovitskiy, Alexander Terenin, Judith Rousseau

CoRR, 2023

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

CoRR, 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

The Cambridge Law Corpus: A Corpus for Legal AI Research.

Andreas Östling, Holli Sargeant, Huiyuan Xie, Ludwig Bull, Alexander Terenin, Leif Jonsson, Måns Magnusson, Felix Steffek

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

Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent.

Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin

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

2022

Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the Compact Case.

Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy

CoRR, 2022

Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces.

Alexander Terenin

CoRR, 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

Learning Contact Dynamics using Physically Structured Neural Networks.

Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson, Marc Peter Deisenroth

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

Aligning Time Series on Incomparable Spaces.

Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth

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

Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models.

Alexander Terenin, Måns Magnusson, Leif Jonsson

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Asynchronous Gibbs Sampling.

Alexander Terenin, Daniel Simpson, David Draper

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Variational Integrator Networks for Physically Structured Embeddings.

Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model.

Alexander Terenin, Shawfeng Dong, David Draper

Stat. Comput., 2019

Pólya Urn Latent Dirichlet Allocation: A Doubly Sparse Massively Parallel Sampler.

Alexander Terenin, Måns Magnusson, Leif Jonsson, David Draper

IEEE Trans. Pattern Anal. Mach. Intell., 2019

Variational Integrator Networks for Physically Meaningful Embeddings.

Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth

CoRR, 2019

2017

A Noninformative Prior on a Space of Distribution Functions.

Alexander Terenin, David Draper

Entropy, 2017

Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods.

Alexander Terenin, Eric P. Xing

CoRR, 2017

2016

GPU-accelerated Gibbs Sampling.

Alexander Terenin, Shawfeng Dong, David Draper

CoRR, 2016