58 citations · 136 across the 13 of their papers we have counts for
9 papers · 1 filter
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…
Nonlinear Hawkes Process with Gaussian Process Self Effects
Noa Malem-Shinitski, Cesar Ojeda, Manfred Opper
Traditionally, Hawkes processes are used to model time--continuous point processes with history dependence. Here we propose an extended model where the self--effects are of both ex…
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…
Tightening Bounds for Variational Inference by Revisiting Perturbation Theory
Robert Bamler, Cheng Zhang, Manfred Opper +1
Variational inference has become one of the most widely used methods in latent variable modeling. In its basic form, variational inference employs a fully factorized variational di…
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…