Alternative estimate of curve exceeding probability of sub-Gaussian random process

Autoregressive Gaussian Random Vectors of First Order

Gaussian Process Random Fields

Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications. We introduce a new approximation for large-scale Gaussian processes, the Gaussian Process Random Field (GPRF), in which local GPs are coupled via pairwise potentials. The GPRF likelihood is a simple, tractable, and paralle...

Some applications of the Malliavin calculus to sub-Gaussian and non-sub-Gaussian random elds

We introduce a boundedness condition on the Malliavin derivative of a random variable to study subGaussian and other non-Gaussian properties of functionals of random …elds, with particular attention to the estimation of suprema. We relate the boundedness of nth Malliavin derivatives to a new class of “sub-nth Gaussian chaos” processes. An expected supremum estimation, extending the DudleyFerniq...

Gaussian Process Latent Random Field

The Gaussian process latent variable model (GPLVM) is an unsupervised probabilistic model for nonlinear dimensionality reduction. A supervised extension, called discriminative GPLVM (DGPLVM), incorporates supervisory information into GPLVM to enhance the classification performance. However, its limitation of the latent space dimensionality to at most C − 1 (C is the number of classes) leads to ...

Expected Number of Local Maxima of Some Gaussian Random Polnomials