128 citations · 204 across the 9 of their papers we have counts for
4 papers · 1 filter
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 Bayesian Inference for a Gaussian Process Density Model
Christian Donner, Manfred Opper
We reconsider a nonparametric density model based on Gaussian processes. By augmenting the model with latent Pólya--Gamma random variables and a latent marked Poisson process we ob…
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…
Inverse Ising problem in continuous time: A latent variable approach
Christian Donner, Manfred Opper
We consider the inverse Ising problem, i.e. the inference of network couplings from observed spin trajectories for a model with continuous time Glauber dynamics. By introducing two…