Alexander Terenin
22 papers · 2020–2025 · 7 venues · across top CS/AI venues
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Achievements
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🐣 Hot Topic Early Bird 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Sprint 🌉 Interdisciplinary Bridge · Silver 🗺️ Taxonomy Completionist (33)
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Keyword Champion
(4)
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Keyword Collector
(88)
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Century Club · Bronze
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Prolific Year · Productive
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Renaissance Researcher
(7)
Top co-authors
Research topics
Machine Learning
(19)
Artificial Intelligence
(15)
Optimization & Theory
(14)
Bayesian & Probabilistic · Artificial Intelligence
(11)
Core Methods
(11)
Bayesian & Probabilistic · Machine Learning
(9)
Mathematics & Optimization
(7)
Core AI
(5)
Mathematics
(4)
Deep Learning
(3)
Natural Language Processing
(2)
Optimization
(2)
Computer Science
(1)
Computer Vision
(1)
Data Science & Analytics
(1)
Keywords
gaussian process
(14)
uncertainty quantification
(5)
matern kernel
(4)
riemannian manifold
(4)
bayesian learning
(3)
posterior sampling
(3)
kernel methods
(3)
bayesian optimization
(3)
inducing point
(3)
matérn kernel
(2)
stationary kernel
(2)
covariance kernel
(2)
topic modeling
(2)
non-euclidean space
(2)
gaussian processes
(2)
motion planning
(2)
dynamical system
(2)
sparse approximation
(2)
variational inference
(2)
stochastic gradient descent
(2)
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
A Unifying Variational Framework for Gaussian Process Motion Planning
AISTATS 2024
Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
NeurIPS 2024
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
JMLR 2024
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces
JMLR 2024
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case
JMLR 2024
Stochastic Gradient Descent for Gaussian Processes Done Right
ICLR 2024
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
NeurIPS 2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
NeurIPS 2023
The Cambridge Law Corpus: A Dataset for Legal AI Research
NeurIPS 2023
Aligning Time Series on Incomparable Spaces
AISTATS 2021
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
CoRL 2021
Learning Contact Dynamics using Physically Structured Neural Networks
AISTATS 2021
Matérn Gaussian Processes on Graphs
AISTATS 2021
Pathwise Conditioning of Gaussian Processes
JMLR 2021
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
NeurIPS 2021
Asynchronous Gibbs Sampling
AISTATS 2020
Efficiently sampling functions from Gaussian process posteriors
ICML 2020
Matérn Gaussian Processes on Riemannian Manifolds
NeurIPS 2020
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
EMNLP 2020
Variational Integrator Networks for Physically Structured Embeddings
AISTATS 2020