Alexander Terenin

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 42

Top Topics

Gaussian Process

Top Venues

J. Mach. Learn. Res.

Publications

Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov, Noémie Jaquier, Michael John Hutchinson, Aditya Ravuri, Leonel Rozo, Alexander Terenin, Viacheslav Borovitskiy
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs. CoRR (2024)
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
Stochastic Gradient Descent for Gaussian Processes Done Right. ICLR (2024)
Qian Xie, Raul Astudillo, Peter I. Frazier, Ziv Scully, Alexander Terenin
Cost-aware Bayesian optimization via the Pandora's Box Gittins index. CoRR (2024)
Lucas Cosier, Rares Iordan, Sicelukwanda N. T. Zwane, Giovanni Franzese, James T. Wilson, Marc Peter Deisenroth, Alexander Terenin, Yasemin Bekiroglu
A Unifying Variational Framework for Gaussian Process Motion Planning. AISTATS (2024)
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. CoRR (2023)
Lucas Cosier, Rares Iordan, Sicelukwanda Zwane, Giovanni Franzese, James T. Wilson, Marc Peter Deisenroth, Alexander Terenin, Yasemin Bekiroglu
A Unifying Variational Framework for Gaussian Process Motion Planning. CoRR (2023)
Paul Rosa, Slava Borovitskiy, Alexander Terenin, Judith Rousseau
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds. NeurIPS (2023)
Andreas Östling, Holli Sargeant, Huiyuan Xie, Ludwig Bull, Alexander Terenin, Leif Jonsson, Måns Magnusson, Felix Steffek
The Cambridge Law Corpus: A Corpus for Legal AI Research. CoRR (2023)
Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces. CoRR (2023)
Paul Rosa, Viacheslav Borovitskiy, Alexander Terenin, Judith Rousseau
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds. CoRR (2023)
Andreas Östling, Holli Sargeant, Huiyuan Xie, Ludwig Bull, Alexander Terenin, Leif Jonsson, Måns Magnusson, Felix Steffek
The Cambridge Law Corpus: A Corpus for Legal AI Research. NeurIPS (2023)
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR (2023)
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. NeurIPS (2023)
Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. CoRR (2022)
Iskander Azangulov, Andrei Smolensky, Alexander Terenin, Viacheslav Borovitskiy
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the Compact Case. CoRR (2022)
Alexander Terenin
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces. CoRR (2022)
Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, Nicolas Durrande
Matérn Gaussian Processes on Graphs. AISTATS (2021)
Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky, Alexander Terenin, Tamim Asfour, Leonel Dario Rozo
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels. CoRL (2021)
Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels. NeurIPS (2021)
Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth
Aligning Time Series on Incomparable Spaces. AISTATS (2021)
Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson, Marc Peter Deisenroth
Learning Contact Dynamics using Physically Structured Neural Networks. CoRR (2021)
James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth
Pathwise Conditioning of Gaussian Processes. J. Mach. Learn. Res. 22 (2021)
Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels. CoRR (2021)
Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky, Alexander Terenin, Tamim Asfour, Leonel Dario Rozo
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels. CoRR (2021)
Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson, Marc Peter Deisenroth
Learning Contact Dynamics using Physically Structured Neural Networks. AISTATS (2021)
James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth
Efficiently sampling functions from Gaussian process posteriors. CoRR (2020)
James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth
Pathwise Conditioning of Gaussian Processes. CoRR (2020)
Alexander Terenin, Daniel Simpson, David Draper
Asynchronous Gibbs Sampling. AISTATS (2020)
Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth
Matern Gaussian processes on Riemannian manifolds. CoRR (2020)
Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth
Aligning Time Series on Incomparable Spaces. CoRR (2020)
Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth
Variational Integrator Networks for Physically Structured Embeddings. AISTATS (2020)
Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, Nicolas Durrande
Matern Gaussian Processes on Graphs. CoRR (2020)
Alexander Terenin, Måns Magnusson, Leif Jonsson
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models. EMNLP (1) (2020)
James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth
Efficiently sampling functions from Gaussian process posteriors. ICML (2020)
Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth
Matérn Gaussian Processes on Riemannian Manifolds. NeurIPS (2020)
Alexander Terenin, Måns Magnusson, Leif Jonsson
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models. CoRR (2019)
Alexander Terenin, Måns Magnusson, Leif Jonsson, David Draper
Pólya Urn Latent Dirichlet Allocation: A Doubly Sparse Massively Parallel Sampler. IEEE Trans. Pattern Anal. Mach. Intell. 41 (7) (2019)
Alexander Terenin, Shawfeng Dong, David Draper
GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model. Stat. Comput. 29 (2) (2019)
Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth
Variational Integrator Networks for Physically Meaningful Embeddings. CoRR (2019)
Alexander Terenin, David Draper
A Noninformative Prior on a Space of Distribution Functions. Entropy 19 (8) (2017)
Alexander Terenin, Eric P. Xing
Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods. CoRR (2017)
Alexander Terenin, Shawfeng Dong, David Draper
GPU-accelerated Gibbs Sampling. CoRR (2016)