← Authors

Alexander Terenin

22 papers · 2020–2025 · 7 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🏃 Academic Marathon (5) 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (8) 🗺️ Taxonomy Completionist (34) 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) 🏆 Keyword Champion (2) 🗃️ Keyword Collector (81) 💎 Century Club (22) ⚡ Prolific Year (5)

Conferences

AISTATS (6) NIPS (6) JMLR (5) ICML (2) CORL (1) EMNLP (1) ICLR (1)

Top co-authors

Viacheslav Borovitskiy (9) Marc Deisenroth (7) Peter Mostowsky (5) Iskander Azangulov (4) Andrei Smolensky (3) Leif Jonsson (2) Steindor Saemundsson (2) Måns Magnusson (2) Noémie Jaquier (2) David Janz (2)

Research topics

Robotics (1)

Keywords

gaussian process (13) uncertainty quantification (5) riemannian manifold (4) matern kernel (4) inducing point (3) bayesian optimization (3) posterior sampling (3) bayesian learning (3) kernel methods (2) matérn kernel (2) dynamical system (2) stationary kernel (2) topic modeling (2) non-euclidean space (2) sparse approximation (2) variational inference (2) motion planning (2) covariance kernel (2) named entity recognition (1) black-box optimization (1)

Papers

Stochastic Poisson Surface Reconstruction with One Solve using Geometric Gaussian Processes ICML 2025
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs JMLR 2025
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees JMLR 2024
Stochastic Gradient Descent for Gaussian Processes Done Right ICLR 2024
Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index NIPS 2024
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case JMLR 2024
A Unifying Variational Framework for Gaussian Process Motion Planning AISTATS 2024
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces JMLR 2024
The Cambridge Law Corpus: A Dataset for Legal AI Research NIPS 2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent NIPS 2023
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds NIPS 2023
Matérn Gaussian Processes on Graphs AISTATS 2021
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels NIPS 2021
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels CORL 2021
Aligning Time Series on Incomparable Spaces AISTATS 2021
Learning Contact Dynamics using Physically Structured Neural Networks AISTATS 2021
Pathwise Conditioning of Gaussian Processes JMLR 2021
Asynchronous Gibbs Sampling AISTATS 2020
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models EMNLP 2020
Matérn Gaussian Processes on Riemannian Manifolds NIPS 2020
Variational Integrator Networks for Physically Structured Embeddings AISTATS 2020
Efficiently sampling functions from Gaussian process posteriors ICML 2020