8 citations · 10 across the 3 of their papers we have counts for
5 papers
Adaptive Inducing Points Selection For Gaussian Processes
Théo Galy-Fajou, Manfred Opper
Gaussian Processes (\textbf{GPs}) are flexible non-parametric models with strong probabilistic interpretation. While being a standard choice for performing inference on time series…
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
Théo Galy-Fajou, Florian Wenzel, Manfred Opper
We propose automated augmented conjugate inference, a new inference method for non-conjugate Gaussian processes (GP) models. Our method automatically constructs an auxiliary variab…
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
Théo Galy-Fajou, Florian Wenzel, Christian Donner +1
We propose a new scalable multi-class Gaussian process classification approach building on a novel modified softmax likelihood function. The new likelihood has two benefits: it lea…
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
Florian Wenzel, Theo Galy-Fajou, Christan Donner +2
We propose a scalable stochastic variational approach to GP classification building on Polya-Gamma data augmentation and inducing points. Unlike former approaches, we obtain closed…
Bayesian Nonlinear Support Vector Machines for Big Data
Florian Wenzel, Theo Galy-Fajou, Matthaeus Deutsch +1
We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the…