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ORCIDAlexander Terenin
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- affiliation: Imperial College London, UK
- affiliation: University of California, Santa Cruz, CA, USA
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2020 – today
- 2024
[j5]Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge:
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. J. Mach. Learn. Res. 25: 26:1-26:36 (2024)- [c15]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: 1315-1323 - [c14]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 - [i23]Qian Xie, Raul Astudillo, Peter I. Frazier, Ziv Scully, Alexander Terenin:
Cost-aware Bayesian optimization via the Pandora's Box Gittins index. CoRR abs/2406.20062 (2024) - [i22]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 abs/2407.08086 (2024) - 2023
- [c13]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 - [c12]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 - [c11]Paul Rosa, Slava Borovitskiy, Alexander Terenin, Judith Rousseau:
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds. NeurIPS 2023 - [i21]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 abs/2301.13088 (2023) - [i20]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 abs/2306.11589 (2023) - [i19]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 abs/2309.00854 (2023) - [i18]Paul Rosa, Viacheslav Borovitskiy, Alexander Terenin, Judith Rousseau:
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds. CoRR abs/2309.10918 (2023) - [i17]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 abs/2309.12269 (2023) - [i16]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 abs/2310.20581 (2023) - 2022
- [i15]Alexander Terenin:
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces. CoRR abs/2202.10613 (2022) - [i14]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 abs/2208.14960 (2022) - [i13]Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge:
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. CoRR abs/2210.07893 (2022) - 2021
- [j4]James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Pathwise Conditioning of Gaussian Processes. J. Mach. Learn. Res. 22: 105:1-105:47 (2021) - [c10]Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth:
Aligning Time Series on Incomparable Spaces. AISTATS 2021: 1036-1044 - [c9]Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson, Marc Peter Deisenroth:
Learning Contact Dynamics using Physically Structured Neural Networks. AISTATS 2021: 2152-2160 - [c8]Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, Nicolas Durrande:
Matérn Gaussian Processes on Graphs. AISTATS 2021: 2593-2601 - [c7]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: 794-805 - [c6]Michael J. 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: 17160-17169 - [i12]Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson, Marc Peter Deisenroth:
Learning Contact Dynamics using Physically Structured Neural Networks. CoRR abs/2102.11206 (2021) - [i11]Michael J. 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 abs/2110.14423 (2021) - [i10]Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky, Alexander Terenin, Tamim Asfour, Leonel Dario Rozo:
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels. CoRR abs/2111.01460 (2021) - 2020
- [c5]Alexander Terenin, Daniel Simpson, David Draper:
Asynchronous Gibbs Sampling. AISTATS 2020: 144-154 - [c4]Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth:
Variational Integrator Networks for Physically Structured Embeddings. AISTATS 2020: 3078-3087 - [c3]Alexander Terenin, Måns Magnusson, Leif Jonsson:
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models. EMNLP (1) 2020: 2925-2934 - [c2]James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Efficiently sampling functions from Gaussian process posteriors. ICML 2020: 10292-10302 - [c1]Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Matérn Gaussian Processes on Riemannian Manifolds. NeurIPS 2020 - [i9]James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Efficiently sampling functions from Gaussian process posteriors. CoRR abs/2002.09309 (2020) - [i8]Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Matern Gaussian processes on Riemannian manifolds. CoRR abs/2006.10160 (2020) - [i7]Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth:
Aligning Time Series on Incomparable Spaces. CoRR abs/2006.12648 (2020) - [i6]Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, Nicolas Durrande:
Matern Gaussian Processes on Graphs. CoRR abs/2010.15538 (2020) - [i5]James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Pathwise Conditioning of Gaussian Processes. CoRR abs/2011.04026 (2020)
2010 – 2019
- 2019
- [j3]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): 1709-1719 (2019) - [j2]Alexander Terenin, Shawfeng Dong, David Draper:
GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model. Stat. Comput. 29(2): 301-310 (2019) - [i4]Alexander Terenin, Måns Magnusson, Leif Jonsson:
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models. CoRR abs/1906.02416 (2019) - [i3]Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth:
Variational Integrator Networks for Physically Meaningful Embeddings. CoRR abs/1910.09349 (2019) - 2017
- [j1]Alexander Terenin, David Draper:
A Noninformative Prior on a Space of Distribution Functions. Entropy 19(8): 391 (2017) - [i2]Alexander Terenin, Eric P. Xing:
Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods. CoRR abs/1711.06719 (2017) - 2016
- [i1]Alexander Terenin, Shawfeng Dong, David Draper:
GPU-accelerated Gibbs Sampling. CoRR abs/1608.04329 (2016)
Coauthor Index
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