Alexander Terenin

Archive

2024

·Talk · University of Waterloo · Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
·Talk · UQSay Seminar · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
·Talk · Cornell Center for Applied Mathematics · Algorithmic Design Principles for Bayesian Optimization
·Poster · Princeton ML Theory Summer School 2024 · Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
·Talk · PhysicsX · Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
·Talk · Carnegie Mellon University · Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
·Talk · UAI 2024 Tutorial · Geometric Probabilistic Models
·Talk · ISBA 2024 · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
·Poster · ICLR 2024 · Stochastic Gradient Descent for Gaussian Processes Done Right
·Poster · AISTATS 2024 · A Unifying Variational Framework for Gaussian Process Motion Planning
·Poster · NYU AI Meets Science Conference 2024 · Multi-objective Bayesian optimization for design of Pareto-optimal current drive profiles in STEP
·Talk · Cornell University · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent

2023

·Poster · NeurIPS 2023 · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
·Poster · NeurIPS 2023 · Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
·Poster · NeurIPS Dataset Track 2023 · The Cambridge Law Corpus: A Dataset for Legal AI Research
·Talk · Université Grenoble Alpes · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
·Talk · ETH Zürich · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
·Talk · University College London · Physically Structured Neural Networks for Smooth and Contact Dynamics
·Talk · Vector Institute · Physically Structured Neural Networks for Smooth and Contact Dynamics
·Talk · University of Tübingen · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · Lancaster University · Pathwise Conditioning and Non-Euclidean Gaussian Processes

2022

·Talk · Google Research · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · Stanford University · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · Carnegie Mellon University · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · Harvard · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · Columbia · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · MIT · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · Google Brain · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · Cornell University · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · Facebook AI Research · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · Princeton University · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · New York University · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · University of Cambridge · Understanding and Scaling Up Sparse Gaussian Processes
·Talk · University of Waterloo · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · Vector Institute · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · International Society for Bayesian Analysis · Pathwise Conditioning and Non-Euclidean Gaussian Processes
·Talk · University of Cambridge · Machines Regret Their Actions Too: A Brief Tutorial on Multi-armed Bandits
·Talk · Alan Turing Institute · Non-Euclidean Matérn Gaussian Processes
·Talk · École Polytechnique Fédérale de Lausanne · Non-Euclidean Matérn Gaussian Processes
·Talk · ETH Zürich · Physically Structured Neural Networks for Smooth and Contact Dynamics
·Talk · University College London · Non-Euclidean Matérn Gaussian Processes
·Talk · University of Cambridge · Matérn Gaussian Processes on Riemannian Manifolds

2021

·Poster · CoRL 2021 · Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
·Talk · Imperial College London PhD viva · Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces
·Poster · NeurIPS 2021 · Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
·Talk · Imperial College London · A Brief Tutorial on Multi-armed Bandits
·Talk · University College London · Physically Structured Neural Networks for Smooth and Contact Dynamics
·Talk · Imperial College Mathematics PhD Symposium · Matérn Gaussian Processes on Graphs
·Talk · RS:S Workshop on Geometry and Topology in Robotics · Gaussian Processes on Riemannian Manifolds for Robotics
·Poster · AISTATS 2021 · Matérn Gaussian Processes on Graphs
·Poster · AISTATS 2021 · Learning Contact Dynamics using Physically Structured Neural Networks
·Poster · AISTATS 2021 · Aligning Time Series on Incomparable Spaces
·Talk · AISTATS 2021 · Matérn Gaussian Processes on Graphs
·Talk · St. Petersburg State University · A Brief Tutorial on Multi-armed Bandits
·Talk · University College London · A Brief Tutorial on Multi-armed Bandits
·Talk · Vector Institute · Pathwise, spectral, and geometric perspectives on Gaussian processes

2020

·Poster · NeurIPS 2020 · Matérn Gaussian Processes on Riemannian Manifolds
·Talk · EMNLP 2020 · Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
·Talk · TheAlgo 2020 · Matérn Gaussian Processes on Riemannian Manifolds
·Talk · University of Oxford · Pathwise, spectral, and geometric perspectives on Gaussian processes
·Talk · Imperial College London · Efficiently sampling functions from Gaussian process posteriors
·Talk · Symposium on Machine Learning and Dynamical Systems 2020 · Efficiently sampling functions from Gaussian process posteriors
·Talk · Imperial College London · Efficiently sampling functions from Gaussian process posteriors
·Talk · GE Research · Pathwise, spectral, and geometric perspectives on Gaussian processes
·Talk · AISTATS 2020 · Asynchronous Gibbs Sampling
·Talk · University College London · Towards physically structured probabilistic reinforcement learning
·Talk · PROWLER.io · Towards physically structured probabilistic reinforcement learning

2019

·Talk · Linköping Workshop on Computational Text Analysis 2019 · Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
·Poster · Workshop on Structural Inference in High-Dimensional Models 2019 · A Piecewise Deterministic Markov Process via (r, θ) swaps in hyperspherical coordinates

2018

·Talk · CRISM Summer School 2018 · Pólya Urn Latent Dirichlet Allocation
·Talk · MaxEnt 2018 · A Piecewise Deterministic Markov Process via (r, θ) swaps in hyperspherical coordinates
·Poster · ISBA 2018 · Pólya Urn Latent Dirichlet Allocation
·Poster · Gregynog Statistical Conference 2018 · Pólya Urn Latent Dirichlet Allocation
·Talk · Carnegie Mellon University · Pólya Urn Latent Dirichlet Allocation

2013

·Undergraduate Research Poster · SHAPE

2024

·NeurIPS 2024 · Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
·Preprint · The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs
·ICLR 2024 · Stochastic Gradient Descent for Gaussian Processes Done Right
·AISTATS 2024 · A Unifying Variational Framework for Gaussian Process Motion Planning
·IEEE Transactions on Plasma Science · Multi-objective Bayesian optimization for design of Pareto-optimal current drive profiles in STEP

2023

·NeurIPS 2023 · Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
·NeurIPS 2023 · Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
·NeurIPS Dataset Track 2023 · The Cambridge Law Corpus: A Dataset for Legal AI Research
·JMLR 2023 · Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
·Preprint · Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces
·Preprint · Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case

2022

·PhD Thesis · Gaussian Processes and Statistical Decision-making in Non-Euclidean spaces

2021

·CoRL 2021 · Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
·NeurIPS 2021 · Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
·AISTATS 2021 · Learning Contact Dynamics using Physically Structured Neural Networks

2020

·JMLR 2021 · Pathwise Conditioning of Gaussian Processes
·AISTATS 2021 · Matérn Gaussian Processes on Graphs
·NeurIPS 2020 · Matérn Gaussian Processes on Riemannian Manifolds
·ICML 2020 · Efficiently Sampling Functions from Gaussian Process Posteriors
·AISTATS 2021 · Aligning Time Series on Incomparable Spaces
·AISTATS 2020 · Variational Integrator Networks for Physically Structured Embeddings

2019

·EMNLP 2020 · Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
·PAMI 2019 · Pólya Urn Latent Dirichlet Allocation
See CV for all papers