Search Results for author: Kurt Cutajar

Found 6 papers, 4 papers with code

Deep Gaussian Processes for Multi-fidelity Modeling

1 code implementation18 Mar 2019 Kurt Cutajar, Mark Pullin, Andreas Damianou, Neil Lawrence, Javier González

Multi-fidelity methods are prominently used when cheaply-obtained, but possibly biased and noisy, observations must be effectively combined with limited or expensive true data in order to construct reliable models.

Decision Making Gaussian Processes +1

Entropic Trace Estimates for Log Determinants

1 code implementation24 Apr 2017 Jack Fitzsimons, Diego Granziol, Kurt Cutajar, Michael Osborne, Maurizio Filippone, Stephen Roberts

The scalable calculation of matrix determinants has been a bottleneck to the widespread application of many machine learning methods such as determinantal point processes, Gaussian processes, generalised Markov random fields, graph models and many others.

Gaussian Processes Point Processes

Bayesian Inference of Log Determinants

no code implementations5 Apr 2017 Jack Fitzsimons, Kurt Cutajar, Michael Osborne, Stephen Roberts, Maurizio Filippone

The log-determinant of a kernel matrix appears in a variety of machine learning problems, ranging from determinantal point processes and generalized Markov random fields, through to the training of Gaussian processes.

Bayesian Inference Gaussian Processes +1

AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models

no code implementations18 Oct 2016 Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, Maurizio Filippone

We investigate the capabilities and limitations of Gaussian process models by jointly exploring three complementary directions: (i) scalable and statistically efficient inference; (ii) flexible kernels; and (iii) objective functions for hyperparameter learning alternative to the marginal likelihood.

General Classification

Random Feature Expansions for Deep Gaussian Processes

1 code implementation ICML 2017 Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone

The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty.

Gaussian Processes Variational Inference

Preconditioning Kernel Matrices

1 code implementation22 Feb 2016 Kurt Cutajar, Michael A. Osborne, John P. Cunningham, Maurizio Filippone

Preconditioning is a common approach to alleviating this issue.

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