Gaussian Processes for Classification
Differentially private regression and classification with sparse Gaussian processes
... with Gaussian processes through the previously published cloaking ...at classification, and modify the Laplace approximation ap- proach to provide differentially private ...perform ...
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Using Gaussian Processes for Rumour Stance Classification in Social Media
... on Gaussian Processes (GP and GP-ICM) and comparison with respect to a set of competitive baselines over two rumour datasets enables us to gain generalisable insight on rumour stance classification ...
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Classification in multi- observational setting using latent Gaussian Processes
... 2.2 Gaussian Processes Due to their seemingly simple nature and interesting analytical properties Gaussian processes have been used extensively in various statistical tasks for ...Wiener ...
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Extrinsic Gaussian processes for regression and classification on manifolds
... Abstract. Gaussian processes (GPs) are very widely used for modeling of un- known functions or surfaces in applications ranging from regression to classifi- cation to spatial ...extrinsic Gaussian ...
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Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data
... As Bayesian probability theory can help construct a uni- fied framework for modeling data and facilitate tuning of the involved parameter and/or hyperparameter, develop- ing a proper Bayesian probabilistic model is ...
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Detecting periodicities with Gaussian processes
... the Gaussian process approach show a strong periodic signal (we have for all genes S = ...the Gaussian process models cannot simply be interpreted as ...
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CONVEX BODIES AND GAUSSIAN PROCESSES
... GB/GC classification of convex bodies, Ito-Nisio singularities from a geometric viewpoint, Gaussian representation of intrinsic volumes, the Wills functional in a Gaussian context, and ...
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Differentially Private Gaussian Processes
... [2014]. It would also be interesting to investigate how to modify the objective function to incorporate the cost of DP noise. The actual method for releasing the corrupted mean function for the output-noise methods has ...
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Gaussian Processes for Machine Learning
... problems. Gaussian Processes for Machine Learning presents one of the most important Bayesian machine learning approaches based on a particularly effective method for placing a prior distribution over the ...
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Bayesian Warped Gaussian Processes
... and classification data; to explicitly model output noise; and to allow for warping functions of unlimited flexibility, which may include flat ...for classification), BWGP behaves as a Swiss Army knife ...
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Detecting periodicities with Gaussian processes
... the Gaussian process approach show a strong periodic signal (we have for all genes S = ...the Gaussian process models cannot simply be interpreted as ...
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Stein Variational Gaussian Processes
... in Gaussian process (GP) models with non-Gaussian likelihoods and large data ...and classification, and a real air quality example with 11440 spatiotemporal observations, showing substantial ...
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Relational Learning with Gaussian Processes
... “Student or Not” while yields better results on 3 out of 4 tasks of “Other or Not”. As future work, it would be interesting to utilize weighted linkages and to compare with other graph kernels. 4.3 Semi-supervised ...
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Gaussian Processes for Ordinal Regression
... Gaussian processes (O’Hagan, 1978; Neal, 1997) have provided a promising non-parametric Bayesian approach to metric regression (Williams and Rasmussen, 1996) and classification prob- lems (Williams ...
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String and Membrane Gaussian Processes
... multivariate Gaussian probability density function (pdf) over training data with the marginal over a smaller set of inducing points multiplied by an approximate conditional pdf (Smola and Bartlett (2001); Lawrence ...
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Gaussian Processes for Regression: A Quick Introduction
... Section 3 explains how to train the classifier, so perhaps we’re presenting things in reverse order! Section 4 handles classification when there are more than two classes. Before we get started, a quick note on ...
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Embarrassingly Parallel Inference for Gaussian Processes
... In addition, we also ran IS-MOE in comparison with the sparse variational GP (Hensman et al., 2013) on a binary classification dataset to distinguish background processes from Higgs-Boson particles. Our ...
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How Deep Are Deep Gaussian Processes?
... Gaussian processes have proved remarkably successful as a tool for various statistical infer- ence and machine learning tasks (Rasmussen and Williams, 2006; Kennedy and O’Hagan, 2001; Higdon et ...the ...
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Variational Fourier Features for Gaussian Processes
... variational classification using Variational Fourier Features, we can use a Gaus- sian approximation to the posterior q(u) and optimize the ELBO with respect to the mean and variance of the approximation, as ...
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Deep Gaussian Processes
... We have introduced a framework for efficient Bayesian training of hierarchical Gaussian process mappings. Our approach approximately marginalises out the latent space, thus allowing for automatic structure ...
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