no code implementations • 4 Nov 2022 • Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl E. Rasmussen
In this work we propose an algorithm for sparse Gaussian process regression which leverages MCMC to sample from the hyperparameter posterior within the variational inducing point framework of Titsias (2009).
no code implementations • 24 Feb 2021 • Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk
Through careful experimentation on the UCI, CIFAR-10, and the UTKFace datasets, we find that the overfitting from overparameterized maximum marginal likelihood, in which the model is "somewhat Bayesian", can in certain scenarios be worse than that from not being Bayesian at all.
1 code implementation • 10 Oct 2019 • Martin Trapp, Robert Peharz, Franz Pernkopf, Carl E. Rasmussen
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs.
1 code implementation • 8 Mar 2019 • David R. Burt, Carl E. Rasmussen, Mark van der Wilk
Excellent variational approximations to Gaussian process posteriors have been developed which avoid the $\mathcal{O}\left(N^3\right)$ scaling with dataset size $N$.
1 code implementation • 12 Sep 2018 • Martin Trapp, Robert Peharz, Carl E. Rasmussen, Franz Pernkopf
In this paper, we introduce a natural and expressive way to tackle these problems, by incorporating GPs in sum-product networks (SPNs), a recently proposed tractable probabilistic model allowing exact and efficient inference.
no code implementations • NeurIPS 2014 • Roger Frigola, Yutian Chen, Carl E. Rasmussen
State-space models have been successfully used for more than fifty years in different areas of science and engineering.
1 code implementation • NeurIPS 2014 • Yarin Gal, Mark van der Wilk, Carl E. Rasmussen
We show that GP performance improves with increasing amounts of data in regression (on flight data with 2 million records) and latent variable modelling (on MNIST).
no code implementations • 17 Dec 2013 • Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen
Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems.
no code implementations • NeurIPS 2013 • Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen
State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems.
no code implementations • NeurIPS 2012 • Michael Osborne, Roman Garnett, Zoubin Ghahramani, David K. Duvenaud, Stephen J. Roberts, Carl E. Rasmussen
Numerical integration is an key component of many problems in scientific computing, statistical modelling, and machine learning.
no code implementations • NeurIPS 2011 • Andrew Mchutchon, Carl E. Rasmussen
This allows the input noise to be recast as output noise proportional to the squared gradient of the GP posterior mean.