6 citations · 7 across the 4 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…