15 citations · 17 across the 3 of their papers we have counts for
6 papers
Sparse Gaussian Processes with Spherical Harmonic Features Revisited
Stefanos Eleftheriadis, Dominic Richards, James Hensman
We revisit the Gaussian process model with spherical harmonic features and study connections between the associated RKHS, its eigenstructure and deep models. Based on this, we intr…
Doubly Sparse Variational Gaussian Processes
Vincent Adam, Stefanos Eleftheriadis, Nicolas Durrande +2
The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their complexity and memory footprint. The two most commonly…
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era
Nicolas Durrande, Vincent Adam, Lucas Bordeaux +2
Banded matrices can be used as precision matrices in several models including linear state-space models, some Gaussian processes, and Gaussian Markov random fields. The aim of the…
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
Hugh Salimbeni, Stefanos Eleftheriadis, James Hensman
The natural gradient method has been used effectively in conjugate Gaussian process models, but the non-conjugate case has been largely unexplored. We examine how natural gradients…
Identification of Gaussian Process State Space Models
Stefanos Eleftheriadis, Thomas F. W. Nicholson, Marc Peter Deisenroth +1
The Gaussian process state space model (GPSSM) is a non-linear dynamical system, where unknown transition and/or measurement mappings are described by GPs. Most research in GPSSMs…
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis
Stefanos Eleftheriadis, Ognjen Rudovic, Marc P. Deisenroth +1
We present a novel approach for supervised domain adaptation that is based upon the probabilistic framework of Gaussian processes (GPs). Specifically, we introduce domain-specific…