412 citations · 1.6k across the 66 of their papers we have counts for
5 papers · 2 filters
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
Tom Gunter, Michael A. Osborne, Roman Garnett +2
We propose a novel sampling framework for inference in probabilistic models: an active learning approach that converges more quickly (in wall-clock time) than Markov chain Monte Ca…
Variational Inference for Gaussian Process Modulated Poisson Processes
Chris Lloyd, Tom Gunter, Michael A. Osborne +1
We present the first fully variational Bayesian inference scheme for continuous Gaussian-process-modulated Poisson processes. Such point processes are used in a variety of domains,…
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
Yves-Laurent Kom Samo, Stephen Roberts
In this paper we propose the first non-parametric Bayesian model using Gaussian Processes to make inference on Poisson Point Processes without resorting to gridding the domain or t…
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning
Thomas Nickson, Michael A Osborne, Steven Reece +1
We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters…
Efficient Bayesian Nonparametric Modelling of Structured Point Processes
Tom Gunter, Chris Lloyd, Michael A. Osborne +1
This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled inde…