Research
I study machine learning and artificial intelligence. Developing a principled understanding of decision-making and explore-exploit tradeoffs is the next key step on the path to creating machine intelligence. The brain evolved to process information efficiently: therefore, so should our algorithms.
Talks
Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
· Carnegie Mellon University
Geometric Probabilistic Models
· Conference on Uncertainty in Artificial Intelligence
Machines Regret Their Actions Too: A Brief Tutorial on Multi-armed Bandits
· University of Cambridge
Research Areas and Selected Papers by Topic
Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
Qian Xie, Raul Astudillo, Peter I. Frazier, Ziv Scully, and Alexander Terenin
NeurIPS 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, and Viacheslav Borovitskiy
Preprint
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces
Iskander Azangulov, Andrei Smolensky, Alexander Terenin, and Viacheslav Borovitskiy
JMLR 2024
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case
Iskander Azangulov, Andrei Smolensky, Alexander Terenin, and Viacheslav Borovitskiy
JMLR 2024
Stochastic Gradient Descent for Gaussian Processes Done Right
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Alexander Terenin, José Miguel Hernández-Lobato, and David Janz
ICLR 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, and Yasemin Bekiroğlu
AISTATS 2024
Multi-objective Bayesian optimisation for design of Pareto-optimal current drive profiles in STEP
Theodore Brown, Stephen Marsden, Vignesh Gopakumar, Alexander Terenin, Hong Ge, and Francis Casson
IEEE Transactions on Plasma Science 2024
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, and Hong Ge
JMLR 2024
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, and Alexander Terenin
NeurIPS 2023
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
Paul Rosa, Viacheslav Borovitskiy, Alexander Terenin, and Judith Rousseau
NeurIPS 2023
The Cambridge Law Corpus: A Dataset for Legal AI Research
Andreas Östling, Holli Sargeant, Huiyuan Xie, Ludwig Bull, Alexander Terenin, Leif Jonsson, Måns Magnusson, and Felix Steffek
NeurIPS 2023
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces
Alexander Terenin
PhD Thesis
Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
Qian Xie, Raul Astudillo, Peter I. Frazier, Ziv Scully, and Alexander Terenin
NeurIPS 2024
Multi-objective Bayesian optimisation for design of Pareto-optimal current drive profiles in STEP
Theodore Brown, Stephen Marsden, Vignesh Gopakumar, Alexander Terenin, Hong Ge, and Francis Casson
IEEE Transactions on Plasma Science 2024
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky, Alexander Terenin, Tamim Asfour, and Leonel Rozo
CoRL 2021
Pathwise Conditioning of Gaussian Processes
James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, and Marc Peter Deisenroth
JMLR 2021
Stochastic Gradient Descent for Gaussian Processes Done Right
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Alexander Terenin, José Miguel Hernández-Lobato, and David Janz
ICLR 2024
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, and Hong Ge
JMLR 2024
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, and Alexander Terenin
NeurIPS 2023
Pathwise Conditioning of Gaussian Processes
James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, and Marc Peter Deisenroth
JMLR 2021
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, and Viacheslav Borovitskiy
Preprint
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces
Iskander Azangulov, Andrei Smolensky, Alexander Terenin, and Viacheslav Borovitskiy
JMLR 2024
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case
Iskander Azangulov, Andrei Smolensky, Alexander Terenin, and Viacheslav Borovitskiy
JMLR 2024
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
Paul Rosa, Viacheslav Borovitskiy, Alexander Terenin, and Judith Rousseau
NeurIPS 2023
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces
Alexander Terenin
PhD Thesis
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky, Alexander Terenin, Tamim Asfour, and Leonel Rozo
CoRL 2021
Matérn Gaussian Processes on Riemannian Manifolds
Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, and Marc Peter Deisenroth
NeurIPS 2020
Variational Integrator Networks for Physically Structured Embeddings
Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, and Marc Peter Deisenroth
AISTATS 2020
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, and Yasemin Bekiroğlu
AISTATS 2024
Multi-objective Bayesian optimisation for design of Pareto-optimal current drive profiles in STEP
Theodore Brown, Stephen Marsden, Vignesh Gopakumar, Alexander Terenin, Hong Ge, and Francis Casson
IEEE Transactions on Plasma Science 2024
The Cambridge Law Corpus: A Dataset for Legal AI Research
Andreas Östling, Holli Sargeant, Huiyuan Xie, Ludwig Bull, Alexander Terenin, Leif Jonsson, Måns Magnusson, and Felix Steffek
NeurIPS 2023
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky, Alexander Terenin, Tamim Asfour, and Leonel Rozo
CoRL 2021
