36 citations · 163 across the 27 of their papers we have counts for
29 papers
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs
Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov +6
Kernels are a fundamental technical primitive in machine learning. In recent years, kernel-based methods such as Gaussian processes are becoming increasingly important in applicati…
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
Paul Rosa, Viacheslav Borovitskiy, Alexander Terenin +1
Gaussian processes are used in many machine learning applications that rely on uncertainty quantification. Recently, computational tools for working with these models in geometric…
Stochastic Gradient Descent for Gaussian Processes Done Right
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán +5
As is well known, both sampling from the posterior and computing the mean of the posterior in Gaussian process regression reduces to solving a large linear system of equations. We…
The Cambridge Law Corpus: A Dataset for Legal AI Research
Andreas Östling, Holli Sargeant, Huiyuan Xie +5
We introduce the Cambridge Law Corpus (CLC), a dataset for legal AI research. It consists of over 250 000 court cases from the UK. Most cases are from the 21st century, but the cor…
A Unifying Variational Framework for Gaussian Process Motion Planning
Lucas Cosier, Rares Iordan, Sicelukwanda Zwane +5
To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints,…
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Jihao Andreas Lin, Javier Antorán, Shreyas Padhy +3
Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems. In general,…
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces
Iskander Azangulov, Andrei Smolensky, Alexander Terenin +1
Gaussian processes are arguably the most important class of spatiotemporal models within machine learning. They encode prior information about the modeled function and can be used…
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
Alexander Terenin, David R. Burt, Artem Artemev +4
Gaussian processes are frequently deployed as part of larger machine learning and decision-making systems, for instance in geospatial modeling, Bayesian optimization, or in latent…
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case
Iskander Azangulov, Andrei Smolensky, Alexander Terenin +1
Gaussian processes are arguably the most important class of spatiotemporal models within machine learning. They encode prior information about the modeled function and can be used…
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces
Alexander Terenin
Bayesian learning using Gaussian processes provides a foundational framework for making decisions in a manner that balances what is known with what could be learned by gathering da…
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy +3
Gaussian processes are machine learning models capable of learning unknown functions in a way that represents uncertainty, thereby facilitating construction of optimal decision-mak…
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky +3
Bayesian optimization is a data-efficient technique which can be used for control parameter tuning, parametric policy adaptation, and structure design in robotics. Many of these pr…
Learning Contact Dynamics using Physically Structured Neural Networks
Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson +1
Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics…
Sliced Multi-Marginal Optimal Transport
Samuel Cohen, Alexander Terenin, Yannik Pitcan +3
Multi-marginal optimal transport enables one to compare multiple probability measures, which increasingly finds application in multi-task learning problems. One practical limitatio…
Pathwise Conditioning of Gaussian Processes
James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin +2
As Gaussian processes are used to answer increasingly complex questions, analytic solutions become scarcer and scarcer. Monte Carlo methods act as a convenient bridge for connectin…
Matérn Gaussian Processes on Graphs
Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin +3
Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information about their properties. Although many differen…
Aligning Time Series on Incomparable Spaces
Samuel Cohen, Giulia Luise, Alexander Terenin +2
Dynamic time warping (DTW) is a useful method for aligning, comparing and combining time series, but it requires them to live in comparable spaces. In this work, we consider a sett…
Matérn Gaussian processes on Riemannian manifolds
Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky +1
Gaussian processes are an effective model class for learning unknown functions, particularly in settings where accurately representing predictive uncertainty is of key importance.…
Efficiently Sampling Functions from Gaussian Process Posteriors
James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin +2
Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model's success hinges upon its ability to faithfully represent predicti…
Variational Integrator Networks for Physically Structured Embeddings
Steindor Saemundsson, Alexander Terenin, Katja Hofmann +1
Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. By leveraging recent work connecting deep neu…
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
Alexander Terenin, Måns Magnusson, Leif Jonsson
To scale non-parametric extensions of probabilistic topic models such as Latent Dirichlet allocation to larger data sets, practitioners rely increasingly on parallel and distribute…
A Piecewise Deterministic Markov Process via swaps in hyperspherical coordinates
Alexander Terenin, Daniel Thorngren
Recently, a class of stochastic processes known as piecewise deterministic Markov processes has been used to define continuous-time Markov chain Monte Carlo algorithms with a numbe…
Comment: A brief survey of the current state of play for Bayesian computation in data science at Big-Data scale
David Draper, Alexander Terenin
We wish to contribute to the discussion of "Comparing Consensus Monte Carlo Strategies for Distributed Bayesian Computation" by offering our views on the current best methods for B…
Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods
Alexander Terenin, Eric P. Xing
Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are finding widespread use in applied statistics and machine learning. These often lead to difficult computational pr…